A fast instance selection method for support vector machines in building extraction
Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as a training dataset. In this research, locality-sensitive hashing (LSH) is adopted for developing a new instance selection method which is ref...
Saved in:
| Published in: | Applied soft computing Vol. 97; no. B; p. 106716 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.12.2020
|
| Subjects: | |
| ISSN: | 1568-4946, 1872-9681, 1872-9681 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as a training dataset. In this research, locality-sensitive hashing (LSH) is adopted for developing a new instance selection method which is referred to as DR.LSH. The intuition of DR.LSH rests on rapidly finding similar and redundant training samples and excluding them from the original dataset. The simple idea of this method alongside its linear computational complexity make it expeditious in coping with massive training data (millions of pixels). DR.LSH is benchmarked against two recently proposed methods on a dataset for building extraction with 23,750,000 samples obtained from the fusion of aerial images and point clouds. The results reveal that DR.LSH outperforms them in terms of both preservation rate and maintaining the generalization ability (classification loss). The source code of DR.LSH can be found in https://github.com/mohaslani/DR.LSH.
•The linear time complexity of DR.LSH makes it suitable for handling big datasets.•DR.LSH is competitive with other state-of-the-art methods in building extraction.•DR.LSH can significantly reduce the number of instances and execution time. |
|---|---|
| AbstractList | Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as a training dataset. In this research, locality-sensitive hashing (LSH) is adopted for developing a new instance selection method which is referred to as DR.LSH. The intuition of DR.LSH rests on rapidly finding similar and redundant training samples and excluding them from the original dataset. The simple idea of this method alongside its linear computational complexity make it expeditious in coping with massive training data (millions of pixels). DR.LSH is benchmarked against two recently proposed methods on a dataset for building extraction with 23,750,000 samples obtained from the fusion of aerial images and point clouds. The results reveal that DR.LSH outperforms them in terms of both preservation rate and maintaining the generalization ability (classification loss). The source code of DR.LSH can be found in https://github.com/mohaslani/DR.LSH. Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as a training dataset. In this research, locality-sensitive hashing (LSH) is adopted for developing a new instance selection method which is referred to as DR.LSH. The intuition of DR.LSH rests on rapidly finding similar and redundant training samples and excluding them from the original dataset. The simple idea of this method alongside its linear computational complexity make it expeditious in coping with massive training data (millions of pixels). DR.LSH is benchmarked against two recently proposed methods on a dataset for building extraction with 23,750,000 samples obtained from the fusion of aerial images and point clouds. The results reveal that DR.LSH outperforms them in terms of both preservation rate and maintaining the generalization ability (classification loss). The source code of DR.LSH can be found in https://github.com/mohaslani/DR.LSH. •The linear time complexity of DR.LSH makes it suitable for handling big datasets.•DR.LSH is competitive with other state-of-the-art methods in building extraction.•DR.LSH can significantly reduce the number of instances and execution time. |
| ArticleNumber | 106716 |
| Author | Aslani, Mohammad Seipel, Stefan |
| Author_xml | – sequence: 1 givenname: Mohammad surname: Aslani fullname: Aslani, Mohammad email: mohammad.aslani@hig.se organization: Department of Computer and Geo-spatial Sciences, University of Gävle, Gävle, Sweden – sequence: 2 givenname: Stefan surname: Seipel fullname: Seipel, Stefan email: Stefan.Seipel@hig.se organization: Department of Computer and Geo-spatial Sciences, University of Gävle, Gävle, Sweden |
| BackLink | https://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-34022$$DView record from Swedish Publication Index (Högskolan i Gävle) https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425980$$DView record from Swedish Publication Index (Uppsala universitet) |
| BookMark | eNqNkbtOwzAUhi1UJErhBZj8AKTYTurYEktVrlIlBi6r5Tgnras0ruykwNvjEMTAUDH5XP7vDJ9P0ahxDSB0QcmUEsqvNlMdnJkywvoBzyk_QmMqcpZILugo1jMukkxm_ASdhrAhEZJMjNHzHFc6tNg2odWNARygBtNa1-AttGtX4sp5HLrdzvkW7-Mqtltt1raBEClcdLYubbPC8NF6_U2eoeNK1wHOf94Jer27fVk8JMun-8fFfJmYjIo2Mammecp1ZSgznBNdkoqbEgyVRBPDRSHSVMs8k3qWs5QzAVzmRDIjyrwAkk7Q5XA3vMOuK9TO2632n8ppq27s21w5v1JdpzI2k-Kf8bVdqTQjjMU4G-LGuxA8VL8AJaqXrjaql6566WqQHiHxBzK21b2UKMfWh9HrAYWobG_Bq2AsxC8prY_aVensIfwLyYKgmw |
| CitedBy_id | crossref_primary_10_1016_j_ins_2024_121738 crossref_primary_10_1016_j_jocm_2023_100467 crossref_primary_10_3390_atmos14071174 crossref_primary_10_1007_s10115_024_02302_4 crossref_primary_10_1016_j_cola_2024_101301 crossref_primary_10_1007_s10115_023_02043_w crossref_primary_10_1007_s00521_022_07974_z crossref_primary_10_1016_j_ins_2021_07_015 crossref_primary_10_1016_j_neunet_2023_07_018 crossref_primary_10_1186_s40537_022_00640_0 crossref_primary_10_1007_s43674_022_00033_z crossref_primary_10_1016_j_asoc_2024_111638 crossref_primary_10_1016_j_ins_2021_06_052 crossref_primary_10_1109_JSTARS_2025_3538662 crossref_primary_10_1109_TKDE_2023_3328952 crossref_primary_10_1016_j_asoc_2022_109695 crossref_primary_10_3390_app15084432 crossref_primary_10_1016_j_patcog_2025_112390 crossref_primary_10_3390_s23187760 |
| Cites_doi | 10.1016/j.rse.2004.06.017 10.1016/j.patrec.2014.08.003 10.4018/jamc.2010102604 10.1162/089976601750264965 10.1109/TGRS.2003.810682 10.1016/j.patrec.2013.01.003 10.1109/JPROC.2015.2487976 10.1137/1.9781611972740.12 10.1109/JSTARS.2017.2768541 10.1109/TNN.2006.883722 10.1109/TGRS.2012.2202912 10.1109/TGRS.2013.2249522 10.1016/j.knosys.2016.05.056 10.1016/j.neucom.2014.10.102 10.1109/JPROC.2015.2494218 10.1016/j.neucom.2007.07.028 10.1162/neco.2007.19.3.816 10.1016/j.asoc.2018.04.016 10.3233/IDA-2012-00558 10.1007/s10462-017-9611-1 10.1016/j.patcog.2017.03.025 10.1109/JSTARS.2009.2012488 10.1109/ACCESS.2018.2819705 10.1016/j.neucom.2013.05.040 10.1016/j.knosys.2016.10.031 10.1016/j.geoderma.2008.05.010 10.1016/S0034-4257(01)00295-4 10.1109/72.809092 10.1016/j.neucom.2007.09.008 10.1016/j.isprsjprs.2010.06.001 10.1016/j.advengsoft.2016.01.008 10.1023/A:1022643204877 10.1109/MGRS.2016.2641240 10.1023/A:1018054314350 10.3390/rs10020303 10.1016/j.asoc.2012.11.005 10.1007/s10462-010-9165-y 10.1109/TPAMI.2011.142 10.1016/j.isprsjprs.2013.10.011 10.1016/j.asoc.2015.08.048 10.1007/s10044-008-0142-x 10.1016/j.knosys.2019.105196 10.1109/TPAMI.2010.188 10.1109/IGARSS.2015.7326952 10.1016/j.rse.2006.03.004 10.1109/TIT.1967.1053964 10.1016/j.knosys.2013.09.023 10.1023/A:1022627411411 |
| ContentType | Journal Article |
| Copyright | 2020 The Author(s) |
| Copyright_xml | – notice: 2020 The Author(s) |
| DBID | 6I. AAFTH AAYXX CITATION ADTPV ALQMA AOWAS D8T D8W ZZAVC ACNBI DF2 |
| DOI | 10.1016/j.asoc.2020.106716 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef SwePub SWEPUB Högskolan i Gävle full text SwePub Articles SWEPUB Freely available online SWEPUB Högskolan i Gävle SwePub Articles full text SWEPUB Uppsala universitet full text SWEPUB Uppsala universitet |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-9681 |
| ExternalDocumentID | oai_DiVA_org_uu_425980 oai_DiVA_org_hig_34022 10_1016_j_asoc_2020_106716 S1568494620306542 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 23M 4.4 457 4G. 53G 5GY 5VS 6I. 6J9 7-5 71M 8P~ AABNK AACTN AAEDT AAEDW AAFTH AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABFNM ABFRF ABJNI ABMAC ABXDB ABYKQ ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HVGLF HZ~ IHE J1W JJJVA KOM M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SDF SDG SES SEW SPC SPCBC SST SSV SSZ T5K UHS UNMZH ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD ADTPV ALQMA AOWAS D8T D8W ZZAVC ACNBI DF2 |
| ID | FETCH-LOGICAL-c418t-c3a1736afc12c660ad0f6cdec190a0c68b833a9749a5723628e697092c8d7be03 |
| ISICitedReferencesCount | 21 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000603366700004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1568-4946 1872-9681 |
| IngestDate | Tue Nov 04 16:43:33 EST 2025 Tue Nov 04 16:41:57 EST 2025 Sat Nov 29 07:03:40 EST 2025 Tue Nov 18 22:37:09 EST 2025 Fri Feb 23 02:46:38 EST 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | B |
| Keywords | Support vector machines Instance selection Big data Data reduction Building extraction |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c418t-c3a1736afc12c660ad0f6cdec190a0c68b833a9749a5723628e697092c8d7be03 |
| OpenAccessLink | https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425980 |
| ParticipantIDs | swepub_primary_oai_DiVA_org_uu_425980 swepub_primary_oai_DiVA_org_hig_34022 crossref_primary_10_1016_j_asoc_2020_106716 crossref_citationtrail_10_1016_j_asoc_2020_106716 elsevier_sciencedirect_doi_10_1016_j_asoc_2020_106716 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-12-01 |
| PublicationDateYYYYMMDD | 2020-12-01 |
| PublicationDate_xml | – month: 12 year: 2020 text: 2020-12-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | Applied soft computing |
| PublicationYear | 2020 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Mirjalili, Lewis (b83) 2016; 95 Keller (b77) 2017 Turker, Koc-San (b65) 2015; 34 Platt (b14) 1999 Salvador, Chan (b79) 2004 Cervantes, Li, Yu (b33) 2006 G.M. Foody, The effect of mis-labeled training data on the accuracy of supervised image classification by SVM, in: 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS, 2015, pp. 4987–4990. Antunes, Gomes, Aguiar (b59) 2018 Gao, Wang, Yang, Li (b63) 2018; 6 Schölkopf, Platt, Shawe-Taylor, Smola, Williamson (b71) 2001; 13 Wang, Kwong (b46) 2010 Arnaiz-González, Díez-Pastor, Rodríguez, García-Osorio (b55) 2016; 107 Cai, Shi, Miao, Hao (b80) 2018; 10 Lyhyaoui, Martinez, Mora, Vaquez, Sancho, Figueiras-Vidal (b29) 1999; 10 López Chau, Li, Yu (b40) 2013; 122 Li, Maguire (b47) 2011; 33 Wang, Shi (b37) 2008; 71 Hastie, Tibshirani, Friedman (b75) 2009 Vapnik (b5) 1998 M.B. de Almeida, A.d.P. Braga, J.P. Braga, SVM-KM: speeding SVMs learning with a priori cluster selection and k-means, in: Proceedings. Vol. 1. Sixth Brazilian Symposium on Neural Networks, RJ, Brazil, 2000, pp. 162–167. Olvera-López, Carrasco-Ochoa, Martínez-Trinidad (b39) 2010; 13 Awrangjeb, Ravanbakhsh, Fraser (b62) 2010; 65 Carbonera, Abel (b44) 2015 Wang, Liu, Kumar, Chang (b57) 2016; 104 Gerke, Xiao (b61) 2014; 87 Nalepa, Kawulok (b19) 2018; 52 Demir, Minello, Bruzzone (b7) 2014; 52 Liu, Wang, Wang, Lv, Konan (b18) 2017; 116 Cervantes, García Lamont, López-Chau, Rodríguez Mazahua, Sergio Ruíz (b22) 2015; 37 Wang, Neskovic, Cooper (b27) 2007 Derrac, García, Herrera (b25) 2010; 1 Cortes, Vapnik (b1) 1995; 20 Zeng, Xu, Xie, Gao (b38) 2008 Foody (b6) 2002; 80 Jiang, Wang (b70) 2017; 69 Guo, Boukir, Chehata (b49) 2010 Breiman (b50) 1996; 24 Guo, Boukir (b52) 2013; 34 Quinlan (b53) 1986; 1 Zhu, Wang, Li, Du (b48) 2020; 190 Garcia, Derrac, Cano, Herrera (b24) 2012; 34 Shin, Cho (b45) 2002 Liu, Huang, Wang (b3) 2018; 11 Shahriari, Swersky, Wang, Adams, de Freitas (b74) 2016; 104 Foody, Mathur (b8) 2004; 93 Yu, Yang, Han (b36) 2003 Foody, Mathur, Sanchez-Hernandez, Boyd (b9) 2006; 104 Huang, Zhang (b64) 2013; 51 Shin, Cho (b41) 2007; 19 D. Boley, D. Cao, Training support vector machine using adaptive clustering, in: Proceedings of the Fourth SIAM International Conference on Data Mining, Florida, USA, 2004. Chang, Guo, Lin, Liu, Lu (b10) 2010 Schmidt, Behrens, Scholten (b21) 2008; 146 Leo Breiman, Jerome Friedman, Charles J. Stone (b51) 1984 Satopaa, Albrecht, Irwin, Raghavan (b78) 2011 Li, Cervantes, Yu (b28) 2012; 16 Abe, Inoue (b42) 2001 Koggalage, Halgamuge (b31) 2004; 2 Cervantes, Li, Yu, Li (b35) 2008; 71 Feurer, Hutter (b72) 2019 Marone, Camara, Ndiaye (b16) 2017 García, Luengo, Herrera (b20) 2015 Olvera-López, Carrasco-Ochoa, Martínez-Trinidad, Kittler (b23) 2010; 34 Gonzalez, Woods (b67) 2008 Pavlov, Mao, Dom (b13) 2000 Rutzinger, Rottensteiner, Pfeifer (b81) 2009; 2 Abe (b11) 2010 Carbonera, Abel (b54) 2018 A. Gionis, P. Indyk, R. Motwani, Similarity search in high dimensions via hashing, in: VLDB ’99 Proceedings of the 25th International Conference on Very Large Data Bases, Edinburgh, Scotland, 1999, pp. 518–529. Ehrgott (b58) 2005 Maulik, Chakraborty (b4) 2017; 5 Zhang, Chen, Whitman, Shyu, Yan, Zhang (b66) 2003; 41 Guo, Boukir (b17) 2015; 51 Clerc (b82) 2011 Kaufmann (b12) 1999 Arnaiz-González, Díez-Pastor, Rodríguez, García-Osorio (b15) 2018; 68 Ishibuchi, Nojima (b69) 2013; 54 Cover, Hart (b84) 1967; 13 Datar, Immorlica, Indyk, Mirrokni (b60) 2004 Shen, Mu, Li, Wu, Gou, Chen (b32) 2016; 172 Wang, Yu, Lapira, Lee (b73) 2013; 13 Roijers, Whiteson (b76) 2017; vol. 11 Liu, Chen, Yu (b43) 2003 Lee, Huang (b26) 2007; 18 Riley, Degloria, Elliot (b68) 1999; 5 10.1016/j.asoc.2020.106716_b56 Hastie (10.1016/j.asoc.2020.106716_b75) 2009 Zeng (10.1016/j.asoc.2020.106716_b38) 2008 Awrangjeb (10.1016/j.asoc.2020.106716_b62) 2010; 65 Jiang (10.1016/j.asoc.2020.106716_b70) 2017; 69 Mirjalili (10.1016/j.asoc.2020.106716_b83) 2016; 95 Shen (10.1016/j.asoc.2020.106716_b32) 2016; 172 Huang (10.1016/j.asoc.2020.106716_b64) 2013; 51 Feurer (10.1016/j.asoc.2020.106716_b72) 2019 Cover (10.1016/j.asoc.2020.106716_b84) 1967; 13 Ishibuchi (10.1016/j.asoc.2020.106716_b69) 2013; 54 10.1016/j.asoc.2020.106716_b2 Guo (10.1016/j.asoc.2020.106716_b17) 2015; 51 Li (10.1016/j.asoc.2020.106716_b47) 2011; 33 Guo (10.1016/j.asoc.2020.106716_b49) 2010 Gonzalez (10.1016/j.asoc.2020.106716_b67) 2008 Satopaa (10.1016/j.asoc.2020.106716_b78) 2011 Marone (10.1016/j.asoc.2020.106716_b16) 2017 Liu (10.1016/j.asoc.2020.106716_b43) 2003 Nalepa (10.1016/j.asoc.2020.106716_b19) 2018; 52 Salvador (10.1016/j.asoc.2020.106716_b79) 2004 Kaufmann (10.1016/j.asoc.2020.106716_b12) 1999 Riley (10.1016/j.asoc.2020.106716_b68) 1999; 5 Demir (10.1016/j.asoc.2020.106716_b7) 2014; 52 Liu (10.1016/j.asoc.2020.106716_b18) 2017; 116 Abe (10.1016/j.asoc.2020.106716_b11) 2010 Cervantes (10.1016/j.asoc.2020.106716_b35) 2008; 71 Shahriari (10.1016/j.asoc.2020.106716_b74) 2016; 104 Schmidt (10.1016/j.asoc.2020.106716_b21) 2008; 146 Wang (10.1016/j.asoc.2020.106716_b27) 2007 Shin (10.1016/j.asoc.2020.106716_b45) 2002 Olvera-López (10.1016/j.asoc.2020.106716_b23) 2010; 34 Derrac (10.1016/j.asoc.2020.106716_b25) 2010; 1 Vapnik (10.1016/j.asoc.2020.106716_b5) 1998 Leo Breiman, Jerome Friedman, Charles J. Stone (10.1016/j.asoc.2020.106716_b51) 1984 Yu (10.1016/j.asoc.2020.106716_b36) 2003 Carbonera (10.1016/j.asoc.2020.106716_b44) 2015 Wang (10.1016/j.asoc.2020.106716_b46) 2010 10.1016/j.asoc.2020.106716_b34 Li (10.1016/j.asoc.2020.106716_b28) 2012; 16 López Chau (10.1016/j.asoc.2020.106716_b40) 2013; 122 Cortes (10.1016/j.asoc.2020.106716_b1) 1995; 20 Zhang (10.1016/j.asoc.2020.106716_b66) 2003; 41 Arnaiz-González (10.1016/j.asoc.2020.106716_b55) 2016; 107 Cervantes (10.1016/j.asoc.2020.106716_b33) 2006 Keller (10.1016/j.asoc.2020.106716_b77) 2017 Koggalage (10.1016/j.asoc.2020.106716_b31) 2004; 2 Liu (10.1016/j.asoc.2020.106716_b3) 2018; 11 Pavlov (10.1016/j.asoc.2020.106716_b13) 2000 Shin (10.1016/j.asoc.2020.106716_b41) 2007; 19 Zhu (10.1016/j.asoc.2020.106716_b48) 2020; 190 Gao (10.1016/j.asoc.2020.106716_b63) 2018; 6 García (10.1016/j.asoc.2020.106716_b20) 2015 Abe (10.1016/j.asoc.2020.106716_b42) 2001 Maulik (10.1016/j.asoc.2020.106716_b4) 2017; 5 Guo (10.1016/j.asoc.2020.106716_b52) 2013; 34 Rutzinger (10.1016/j.asoc.2020.106716_b81) 2009; 2 Garcia (10.1016/j.asoc.2020.106716_b24) 2012; 34 Arnaiz-González (10.1016/j.asoc.2020.106716_b15) 2018; 68 10.1016/j.asoc.2020.106716_b30 Wang (10.1016/j.asoc.2020.106716_b57) 2016; 104 Ehrgott (10.1016/j.asoc.2020.106716_b58) 2005 Cervantes (10.1016/j.asoc.2020.106716_b22) 2015; 37 Antunes (10.1016/j.asoc.2020.106716_b59) 2018 Gerke (10.1016/j.asoc.2020.106716_b61) 2014; 87 Lee (10.1016/j.asoc.2020.106716_b26) 2007; 18 Olvera-López (10.1016/j.asoc.2020.106716_b39) 2010; 13 Foody (10.1016/j.asoc.2020.106716_b8) 2004; 93 Wang (10.1016/j.asoc.2020.106716_b37) 2008; 71 Foody (10.1016/j.asoc.2020.106716_b9) 2006; 104 Wang (10.1016/j.asoc.2020.106716_b73) 2013; 13 Clerc (10.1016/j.asoc.2020.106716_b82) 2011 Foody (10.1016/j.asoc.2020.106716_b6) 2002; 80 Breiman (10.1016/j.asoc.2020.106716_b50) 1996; 24 Schölkopf (10.1016/j.asoc.2020.106716_b71) 2001; 13 Roijers (10.1016/j.asoc.2020.106716_b76) 2017; vol. 11 Datar (10.1016/j.asoc.2020.106716_b60) 2004 Cai (10.1016/j.asoc.2020.106716_b80) 2018; 10 Lyhyaoui (10.1016/j.asoc.2020.106716_b29) 1999; 10 Quinlan (10.1016/j.asoc.2020.106716_b53) 1986; 1 Carbonera (10.1016/j.asoc.2020.106716_b54) 2018 Turker (10.1016/j.asoc.2020.106716_b65) 2015; 34 Platt (10.1016/j.asoc.2020.106716_b14) 1999 Chang (10.1016/j.asoc.2020.106716_b10) 2010 |
| References_xml | – volume: 34 start-page: 133 year: 2010 end-page: 143 ident: b23 article-title: A review of instance selection methods publication-title: Artif. Intell. Rev. – year: 2009 ident: b75 article-title: The Elements of Statistical Learning. Data Mining, Inference, and Prediction – volume: 116 start-page: 58 year: 2017 end-page: 73 ident: b18 article-title: An efficient instance selection algorithm to reconstruct training set for support vector machine publication-title: Knowl.-Based Syst. – volume: 69 start-page: 94 year: 2017 end-page: 106 ident: b70 article-title: Error estimation based on variance analysis of k-fold cross-validation publication-title: Pattern Recognit. – volume: 146 start-page: 138 year: 2008 end-page: 146 ident: b21 article-title: Instance selection and classification tree analysis for large spatial datasets in digital soil mapping publication-title: Geoderma – volume: 13 start-page: 131 year: 2010 end-page: 141 ident: b39 article-title: A new fast prototype selection method based on clustering publication-title: Pattern Anal. Appl. – volume: 34 start-page: 417 year: 2012 end-page: 435 ident: b24 article-title: Prototype selection for nearest neighbor classification: Taxonomy and empirical study publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 1 start-page: 60 year: 2010 end-page: 92 ident: b25 article-title: A survey on evolutionary instance selection and generation publication-title: Int. J. Appl. Metaheuristic Comput. – volume: 104 start-page: 148 year: 2016 end-page: 175 ident: b74 article-title: Taking the human out of the loop: A review of Bayesian optimization publication-title: Proc. IEEE – volume: 2 start-page: 11 year: 2009 end-page: 20 ident: b81 article-title: A comparison of evaluation techniques for building extraction from airborne laser scanning publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – start-page: 572 year: 2006 end-page: 582 ident: b33 publication-title: Support Vector Machine Classification Based on Fuzzy Clustering for Large Data Sets BT - MICAI 2006: Advances in Artificial Intelligence – volume: 20 start-page: 273 year: 1995 end-page: 297 ident: b1 article-title: Support-vector networks publication-title: Mach. Learn. – start-page: 185 year: 1999 end-page: 208 ident: b14 article-title: Fast training of support vector machines using sequential minimal optimization publication-title: Advances in Kernel Methods: Support Vector Learning – volume: 104 start-page: 1 year: 2006 end-page: 14 ident: b9 article-title: Training set size requirements for the classification of a specific class publication-title: Remote Sens. Environ. – start-page: 306 year: 2003 end-page: 315 ident: b36 article-title: Classifying large data sets using SVMs with hierarchical clusters publication-title: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – start-page: 219 year: 2000 end-page: 222 ident: b13 article-title: Scaling-up support vector machines using boosting algorithm publication-title: Proceedings 15th International Conference on Pattern Recognition, Vol. 2 – volume: 24 start-page: 123 year: 1996 end-page: 140 ident: b50 article-title: Bagging predictors publication-title: Mach. Learn. – volume: 52 start-page: 857 year: 2018 end-page: 900 ident: b19 article-title: Selecting training sets for support vector machines: a review publication-title: Artif. Intell. Rev. – year: 2010 ident: b11 article-title: Support vector machines for pattern classification publication-title: Advances in Computer Vision and Pattern Recognition – volume: 52 start-page: 1272 year: 2014 end-page: 1284 ident: b7 article-title: Definition of effective training sets for supervised classification of remote sensing images by a novel cost-sensitive active learning method publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 18 start-page: 1 year: 2007 end-page: 13 ident: b26 article-title: Reduced support vector machines: A statistical theory publication-title: IEEE Trans. Neural Netw. – volume: 33 start-page: 1189 year: 2011 end-page: 1201 ident: b47 article-title: Selecting critical patterns based on local geometrical and statistical information publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 16 start-page: 897 year: 2012 end-page: 914 ident: b28 article-title: Fast classification for large data sets via random selection clustering and Support Vector Machines publication-title: Intell. Data Anal. – start-page: 308 year: 2001 end-page: 313 ident: b42 article-title: Fast training of support vector machines by extracting boundary data publication-title: International Conference on Artificial Neural Networks – reference: G.M. Foody, The effect of mis-labeled training data on the accuracy of supervised image classification by SVM, in: 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS, 2015, pp. 4987–4990. – start-page: 37 year: 2010 end-page: 40 ident: b49 article-title: Support vectors selection for supervised learning using an ensemble approach publication-title: 20th International Conference on Pattern Recognition – volume: 6 start-page: 22034 year: 2018 end-page: 22045 ident: b63 article-title: Building extraction from RGB VHR images using shifted shadow algorithm publication-title: IEEE Access – volume: 13 start-page: 1193 year: 2013 end-page: 1205 ident: b73 article-title: A modified support vector data description based novelty detection approach for machinery components publication-title: Appl. Soft Comput. – volume: 5 start-page: 23 year: 1999 end-page: 27 ident: b68 article-title: A terrain ruggedness index that quantifies topographic heterogeneity publication-title: Int. J. Sci. – reference: D. Boley, D. Cao, Training support vector machine using adaptive clustering, in: Proceedings of the Fourth SIAM International Conference on Data Mining, Florida, USA, 2004. – volume: 34 start-page: 58 year: 2015 end-page: 69 ident: b65 article-title: Building extraction from high-resolution optical spaceborne images using the integration of support vector machine (SVM) classification, Hough transformation and perceptual grouping publication-title: Int. J. Appl. Earth Obs. Geoinf. – reference: A. Gionis, P. Indyk, R. Motwani, Similarity search in high dimensions via hashing, in: VLDB ’99 Proceedings of the 25th International Conference on Very Large Data Bases, Edinburgh, Scotland, 1999, pp. 518–529. – volume: 10 start-page: 303 year: 2018 ident: b80 article-title: Accuracy assessment measures for object extraction from remote sensing images publication-title: Remote Sens. – volume: vol. 11 start-page: 1 year: 2017 end-page: 129 ident: b76 publication-title: Multi-Objective Decision Making – start-page: 320 year: 2015 ident: b20 publication-title: Data Preprocessing in Data Mining, Vol. 72 – volume: 41 start-page: 872 year: 2003 end-page: 882 ident: b66 article-title: A progressive morphological filter for removing nonground measurements from airborne LIDAR data publication-title: IEEE Trans. Geosci. Remote Sens. – start-page: 253 year: 2004 end-page: 262 ident: b60 article-title: Locality-sensitive hashing scheme based on p-stable distributions publication-title: Proceedings of the Twentieth Annual Symposium on Computational Geometry – start-page: 323 year: 2005 ident: b58 article-title: Multicriteria Optimization – volume: 11 start-page: 1144 year: 2018 end-page: 1157 ident: b3 article-title: Hyperspectral image classification with kernel-based least-squares support vector machines in sum space publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – start-page: 1390 year: 2010 end-page: 1395 ident: b46 article-title: Sample selection based on maximum entropy for support vector machines publication-title: 2010 International Conference on Machine Learning and Cybernetics, Vol. 3 – year: 2008 ident: b67 article-title: Digital Image Processing – start-page: 768 year: 1998 ident: b5 article-title: Statistical Learning Theory – start-page: 147 year: 1999 end-page: 168 ident: b12 article-title: Solving the quadratic programming problem arising in support vector classification publication-title: Advances in Kernel Methods: Support Vector Learning – volume: 104 start-page: 34 year: 2016 end-page: 57 ident: b57 article-title: Learning to hash for indexing big data-a survey publication-title: Proc. IEEE – start-page: 2353 year: 2017 end-page: 2356 ident: b16 article-title: LSIS: Large scale instance selection algorithm for big data publication-title: 3rd IEEE International Conference on Computer and Communications – volume: 51 start-page: 112 year: 2015 end-page: 119 ident: b17 article-title: Fast data selection for SVM training using ensemble margin publication-title: Pattern Recognit. Lett. – volume: 13 start-page: 1443 year: 2001 end-page: 1471 ident: b71 article-title: Estimating support of a high-dimensional distribution publication-title: Neural Comput. – start-page: 991 year: 2008 end-page: 996 ident: b38 article-title: A geometric approach to train SVM on very large data sets publication-title: 3rd International Conference on Intelligent System and Knowledge Engineering, Vol. 1 – volume: 80 start-page: 185 year: 2002 end-page: 201 ident: b6 article-title: Status of land cover classification accuracy assessment publication-title: Remote Sens. Environ. – volume: 68 start-page: 651 year: 2018 end-page: 666 ident: b15 article-title: Local sets for multi-label instance selection publication-title: Appl. Soft Comput. – volume: 87 start-page: 78 year: 2014 end-page: 92 ident: b61 article-title: Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification publication-title: ISPRS J. Photogramm. Remote Sens. – start-page: 469 year: 2002 end-page: 474 ident: b45 article-title: Pattern selection for support vector classifiers publication-title: International Conference on Intelligent Data Engineering and Automated Learning – volume: 2 start-page: 57 year: 2004 end-page: 65 ident: b31 article-title: Reducing the number of training samples for Fast Support Vector Machine Classification publication-title: Neural Inf. Process.-Lett. Rev. – start-page: 61 year: 2007 end-page: 84 ident: b27 article-title: Selecting data for fast support vector machines training publication-title: Trends in Neural Computation – start-page: 233 year: 2010 end-page: 240 ident: b10 article-title: Tree decomposition for large-scale SVM problems publication-title: 2010 International Conference on Technologies and Applications of Artificial Intelligence – volume: 10 start-page: 1474 year: 1999 end-page: 1481 ident: b29 article-title: Sample selection via clustering to construct support vector-like classifiers publication-title: IEEE Trans. Neural Netw. – reference: M.B. de Almeida, A.d.P. Braga, J.P. Braga, SVM-KM: speeding SVMs learning with a priori cluster selection and k-means, in: Proceedings. Vol. 1. Sixth Brazilian Symposium on Neural Networks, RJ, Brazil, 2000, pp. 162–167. – volume: 107 start-page: 83 year: 2016 end-page: 95 ident: b55 article-title: Instance selection of linear complexity for big data publication-title: Knowl.-Based Syst. – volume: 65 start-page: 457 year: 2010 end-page: 467 ident: b62 article-title: Automatic detection of residential buildings using LIDAR data and multispectral imagery publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 37 start-page: 787 year: 2015 end-page: 798 ident: b22 article-title: Data selection based on decision tree for SVM classification on large data sets publication-title: Appl. Soft Comput. – start-page: 166 year: 2011 end-page: 171 ident: b78 article-title: Finding a ”kneedle” in a haystack: Detecting knee points in system behavior publication-title: 2011 31st International Conference on Distributed Computing Systems Workshops – volume: 51 start-page: 257 year: 2013 end-page: 272 ident: b64 article-title: An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery publication-title: IEEE Trans. Geosci. Remote Sens. – start-page: 3199 year: 2003 end-page: 3202 ident: b43 article-title: Extract candidates of support vector from training set publication-title: Proceedings of the 2003 International Conference on Machine Learning and Cybernetics, Vol. 5 – volume: 190 year: 2020 ident: b48 article-title: NearCount: Selecting critical instances based on the cited counts of nearest neighbors publication-title: Knowl.-Based Syst. – start-page: 368 year: 1984 ident: b51 article-title: Classification and Regression Trees – volume: 13 start-page: 21 year: 1967 end-page: 27 ident: b84 article-title: Nearest neighbor pattern classification publication-title: IEEE Trans. Inform. Theory – start-page: 768 year: 2015 end-page: 774 ident: b44 article-title: A density-based approach for instance selection publication-title: 27th International Conference on Tools with Artificial Intelligence – volume: 34 start-page: 603 year: 2013 end-page: 609 ident: b52 article-title: Margin-based ordered aggregation for ensemble pruning publication-title: Pattern Recognit. Lett. – start-page: 576 year: 2004 end-page: 584 ident: b79 article-title: Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms publication-title: 16th IEEE International Conference on Tools with Artificial Intelligence – volume: 71 start-page: 611 year: 2008 end-page: 619 ident: b35 article-title: Support vector machine classification for large data sets via minimum enclosing ball clustering publication-title: Neurocomputing – volume: 122 start-page: 198 year: 2013 end-page: 209 ident: b40 article-title: Convex and concave hulls for classification with support vector machine publication-title: Neurocomputing – volume: 1 start-page: 81 year: 1986 end-page: 106 ident: b53 article-title: Induction of decision trees publication-title: Mach. Learn. – volume: 54 start-page: 22 year: 2013 end-page: 31 ident: b69 article-title: Repeated double cross-validation for choosing a single solution in evolutionary multi-objective fuzzy classifier design publication-title: Knowl.-Based Syst. – volume: 71 start-page: 2772 year: 2008 end-page: 2781 ident: b37 article-title: Selecting valuable training samples for SVMs via data structure analysis publication-title: Neurocomputing – volume: 19 start-page: 816 year: 2007 end-page: 855 ident: b41 article-title: Neighborhood property-based pattern selection for support vector machines publication-title: Neural Comput. – volume: 95 start-page: 51 year: 2016 end-page: 67 ident: b83 article-title: The whale optimization algorithm publication-title: Adv. Eng. Softw. – start-page: 3 year: 2019 end-page: 33 ident: b72 article-title: Hyperparameter optimization publication-title: Automated Machine Learning: Methods, Systems, Challenges – volume: 172 start-page: 189 year: 2016 end-page: 197 ident: b32 article-title: Large-scale support vector machine classification with redundant data reduction publication-title: Neurocomputing – year: 2017 ident: b77 article-title: Multi-Objective Optimization in Theory and Practice I: Classical Methods – volume: 93 start-page: 107 year: 2004 end-page: 117 ident: b8 article-title: Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification publication-title: Remote Sens. Environ. – start-page: 286 year: 2018 end-page: 292 ident: b54 article-title: Efficient instance selection based on spatial abstraction publication-title: 30th International Conference on Tools with Artificial Intelligence – volume: 5 start-page: 33 year: 2017 end-page: 52 ident: b4 article-title: Remote Sensing Image Classification: A survey of support-vector-machine-based advanced techniques publication-title: IEEE Geosci. Remote Sens. Mag. – start-page: 3 year: 2011 end-page: 36 ident: b82 article-title: From theory to practice in particle swarm optimization publication-title: Handbook of Swarm Intelligence: Concepts, Principles and Applications – start-page: 237 year: 2018 end-page: 240 ident: b59 article-title: Knee/elbow estimation based on first derivative threshold publication-title: 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications – volume: 93 start-page: 107 issue: 1 year: 2004 ident: 10.1016/j.asoc.2020.106716_b8 article-title: Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2004.06.017 – volume: 34 start-page: 58 year: 2015 ident: 10.1016/j.asoc.2020.106716_b65 article-title: Building extraction from high-resolution optical spaceborne images using the integration of support vector machine (SVM) classification, Hough transformation and perceptual grouping publication-title: Int. J. Appl. Earth Obs. Geoinf. – start-page: 308 year: 2001 ident: 10.1016/j.asoc.2020.106716_b42 article-title: Fast training of support vector machines by extracting boundary data – volume: 51 start-page: 112 year: 2015 ident: 10.1016/j.asoc.2020.106716_b17 article-title: Fast data selection for SVM training using ensemble margin publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2014.08.003 – volume: 1 start-page: 60 issue: 1 year: 2010 ident: 10.1016/j.asoc.2020.106716_b25 article-title: A survey on evolutionary instance selection and generation publication-title: Int. J. Appl. Metaheuristic Comput. doi: 10.4018/jamc.2010102604 – start-page: 991 year: 2008 ident: 10.1016/j.asoc.2020.106716_b38 article-title: A geometric approach to train SVM on very large data sets – volume: 13 start-page: 1443 issue: 7 year: 2001 ident: 10.1016/j.asoc.2020.106716_b71 article-title: Estimating support of a high-dimensional distribution publication-title: Neural Comput. doi: 10.1162/089976601750264965 – volume: 41 start-page: 872 issue: 4 year: 2003 ident: 10.1016/j.asoc.2020.106716_b66 article-title: A progressive morphological filter for removing nonground measurements from airborne LIDAR data publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2003.810682 – volume: 34 start-page: 603 issue: 6 year: 2013 ident: 10.1016/j.asoc.2020.106716_b52 article-title: Margin-based ordered aggregation for ensemble pruning publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2013.01.003 – volume: 104 start-page: 34 issue: 1 year: 2016 ident: 10.1016/j.asoc.2020.106716_b57 article-title: Learning to hash for indexing big data-a survey publication-title: Proc. IEEE doi: 10.1109/JPROC.2015.2487976 – ident: 10.1016/j.asoc.2020.106716_b34 doi: 10.1137/1.9781611972740.12 – start-page: 253 year: 2004 ident: 10.1016/j.asoc.2020.106716_b60 article-title: Locality-sensitive hashing scheme based on p-stable distributions – volume: 11 start-page: 1144 issue: 4 year: 2018 ident: 10.1016/j.asoc.2020.106716_b3 article-title: Hyperspectral image classification with kernel-based least-squares support vector machines in sum space publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2017.2768541 – volume: 18 start-page: 1 issue: 1 year: 2007 ident: 10.1016/j.asoc.2020.106716_b26 article-title: Reduced support vector machines: A statistical theory publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2006.883722 – volume: 51 start-page: 257 issue: 1 year: 2013 ident: 10.1016/j.asoc.2020.106716_b64 article-title: An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2012.2202912 – volume: 52 start-page: 1272 issue: 2 year: 2014 ident: 10.1016/j.asoc.2020.106716_b7 article-title: Definition of effective training sets for supervised classification of remote sensing images by a novel cost-sensitive active learning method publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2013.2249522 – volume: 107 start-page: 83 year: 2016 ident: 10.1016/j.asoc.2020.106716_b55 article-title: Instance selection of linear complexity for big data publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2016.05.056 – ident: 10.1016/j.asoc.2020.106716_b30 – volume: 172 start-page: 189 year: 2016 ident: 10.1016/j.asoc.2020.106716_b32 article-title: Large-scale support vector machine classification with redundant data reduction publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.10.102 – volume: 104 start-page: 148 issue: 1 year: 2016 ident: 10.1016/j.asoc.2020.106716_b74 article-title: Taking the human out of the loop: A review of Bayesian optimization publication-title: Proc. IEEE doi: 10.1109/JPROC.2015.2494218 – volume: 71 start-page: 611 issue: 4 year: 2008 ident: 10.1016/j.asoc.2020.106716_b35 article-title: Support vector machine classification for large data sets via minimum enclosing ball clustering publication-title: Neurocomputing doi: 10.1016/j.neucom.2007.07.028 – volume: 19 start-page: 816 issue: 3 year: 2007 ident: 10.1016/j.asoc.2020.106716_b41 article-title: Neighborhood property-based pattern selection for support vector machines publication-title: Neural Comput. doi: 10.1162/neco.2007.19.3.816 – volume: 68 start-page: 651 year: 2018 ident: 10.1016/j.asoc.2020.106716_b15 article-title: Local sets for multi-label instance selection publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2018.04.016 – volume: 16 start-page: 897 issue: 6 year: 2012 ident: 10.1016/j.asoc.2020.106716_b28 article-title: Fast classification for large data sets via random selection clustering and Support Vector Machines publication-title: Intell. Data Anal. doi: 10.3233/IDA-2012-00558 – start-page: 1390 year: 2010 ident: 10.1016/j.asoc.2020.106716_b46 article-title: Sample selection based on maximum entropy for support vector machines – volume: 52 start-page: 857 issue: 2 year: 2018 ident: 10.1016/j.asoc.2020.106716_b19 article-title: Selecting training sets for support vector machines: a review publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-017-9611-1 – volume: 69 start-page: 94 year: 2017 ident: 10.1016/j.asoc.2020.106716_b70 article-title: Error estimation based on variance analysis of k-fold cross-validation publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2017.03.025 – start-page: 768 year: 1998 ident: 10.1016/j.asoc.2020.106716_b5 – volume: 2 start-page: 57 year: 2004 ident: 10.1016/j.asoc.2020.106716_b31 article-title: Reducing the number of training samples for Fast Support Vector Machine Classification publication-title: Neural Inf. Process.-Lett. Rev. – start-page: 237 year: 2018 ident: 10.1016/j.asoc.2020.106716_b59 article-title: Knee/elbow estimation based on first derivative threshold – start-page: 185 year: 1999 ident: 10.1016/j.asoc.2020.106716_b14 article-title: Fast training of support vector machines using sequential minimal optimization – volume: 2 start-page: 11 issue: 1 year: 2009 ident: 10.1016/j.asoc.2020.106716_b81 article-title: A comparison of evaluation techniques for building extraction from airborne laser scanning publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2009.2012488 – start-page: 768 year: 2015 ident: 10.1016/j.asoc.2020.106716_b44 article-title: A density-based approach for instance selection – volume: 6 start-page: 22034 year: 2018 ident: 10.1016/j.asoc.2020.106716_b63 article-title: Building extraction from RGB VHR images using shifted shadow algorithm publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2819705 – year: 2010 ident: 10.1016/j.asoc.2020.106716_b11 article-title: Support vector machines for pattern classification – volume: 122 start-page: 198 year: 2013 ident: 10.1016/j.asoc.2020.106716_b40 article-title: Convex and concave hulls for classification with support vector machine publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.05.040 – volume: 116 start-page: 58 year: 2017 ident: 10.1016/j.asoc.2020.106716_b18 article-title: An efficient instance selection algorithm to reconstruct training set for support vector machine publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2016.10.031 – volume: 146 start-page: 138 issue: 1 year: 2008 ident: 10.1016/j.asoc.2020.106716_b21 article-title: Instance selection and classification tree analysis for large spatial datasets in digital soil mapping publication-title: Geoderma doi: 10.1016/j.geoderma.2008.05.010 – start-page: 576 year: 2004 ident: 10.1016/j.asoc.2020.106716_b79 article-title: Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms – start-page: 572 year: 2006 ident: 10.1016/j.asoc.2020.106716_b33 – volume: 80 start-page: 185 issue: 1 year: 2002 ident: 10.1016/j.asoc.2020.106716_b6 article-title: Status of land cover classification accuracy assessment publication-title: Remote Sens. Environ. doi: 10.1016/S0034-4257(01)00295-4 – volume: 10 start-page: 1474 issue: 6 year: 1999 ident: 10.1016/j.asoc.2020.106716_b29 article-title: Sample selection via clustering to construct support vector-like classifiers publication-title: IEEE Trans. Neural Netw. doi: 10.1109/72.809092 – volume: 71 start-page: 2772 issue: 13 year: 2008 ident: 10.1016/j.asoc.2020.106716_b37 article-title: Selecting valuable training samples for SVMs via data structure analysis publication-title: Neurocomputing doi: 10.1016/j.neucom.2007.09.008 – start-page: 469 year: 2002 ident: 10.1016/j.asoc.2020.106716_b45 article-title: Pattern selection for support vector classifiers – volume: 65 start-page: 457 issue: 5 year: 2010 ident: 10.1016/j.asoc.2020.106716_b62 article-title: Automatic detection of residential buildings using LIDAR data and multispectral imagery publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2010.06.001 – volume: 95 start-page: 51 year: 2016 ident: 10.1016/j.asoc.2020.106716_b83 article-title: The whale optimization algorithm publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2016.01.008 – volume: 1 start-page: 81 issue: 1 year: 1986 ident: 10.1016/j.asoc.2020.106716_b53 article-title: Induction of decision trees publication-title: Mach. Learn. doi: 10.1023/A:1022643204877 – start-page: 147 year: 1999 ident: 10.1016/j.asoc.2020.106716_b12 article-title: Solving the quadratic programming problem arising in support vector classification – year: 2017 ident: 10.1016/j.asoc.2020.106716_b77 – volume: 5 start-page: 33 issue: 1 year: 2017 ident: 10.1016/j.asoc.2020.106716_b4 article-title: Remote Sensing Image Classification: A survey of support-vector-machine-based advanced techniques publication-title: IEEE Geosci. Remote Sens. Mag. doi: 10.1109/MGRS.2016.2641240 – start-page: 61 year: 2007 ident: 10.1016/j.asoc.2020.106716_b27 article-title: Selecting data for fast support vector machines training – volume: 24 start-page: 123 issue: 2 year: 1996 ident: 10.1016/j.asoc.2020.106716_b50 article-title: Bagging predictors publication-title: Mach. Learn. doi: 10.1023/A:1018054314350 – start-page: 3 year: 2019 ident: 10.1016/j.asoc.2020.106716_b72 article-title: Hyperparameter optimization – year: 2009 ident: 10.1016/j.asoc.2020.106716_b75 – start-page: 320 year: 2015 ident: 10.1016/j.asoc.2020.106716_b20 – year: 2008 ident: 10.1016/j.asoc.2020.106716_b67 – volume: 5 start-page: 23 year: 1999 ident: 10.1016/j.asoc.2020.106716_b68 article-title: A terrain ruggedness index that quantifies topographic heterogeneity publication-title: Int. J. Sci. – volume: 10 start-page: 303 issue: 2 year: 2018 ident: 10.1016/j.asoc.2020.106716_b80 article-title: Accuracy assessment measures for object extraction from remote sensing images publication-title: Remote Sens. doi: 10.3390/rs10020303 – volume: 13 start-page: 1193 issue: 2 year: 2013 ident: 10.1016/j.asoc.2020.106716_b73 article-title: A modified support vector data description based novelty detection approach for machinery components publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2012.11.005 – volume: 34 start-page: 133 issue: 2 year: 2010 ident: 10.1016/j.asoc.2020.106716_b23 article-title: A review of instance selection methods publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-010-9165-y – start-page: 166 year: 2011 ident: 10.1016/j.asoc.2020.106716_b78 article-title: Finding a ”kneedle” in a haystack: Detecting knee points in system behavior – volume: 34 start-page: 417 issue: 3 year: 2012 ident: 10.1016/j.asoc.2020.106716_b24 article-title: Prototype selection for nearest neighbor classification: Taxonomy and empirical study publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2011.142 – start-page: 368 year: 1984 ident: 10.1016/j.asoc.2020.106716_b51 – volume: 87 start-page: 78 year: 2014 ident: 10.1016/j.asoc.2020.106716_b61 article-title: Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2013.10.011 – volume: 37 start-page: 787 year: 2015 ident: 10.1016/j.asoc.2020.106716_b22 article-title: Data selection based on decision tree for SVM classification on large data sets publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2015.08.048 – start-page: 3 year: 2011 ident: 10.1016/j.asoc.2020.106716_b82 article-title: From theory to practice in particle swarm optimization – volume: vol. 11 start-page: 1 year: 2017 ident: 10.1016/j.asoc.2020.106716_b76 – volume: 13 start-page: 131 issue: 2 year: 2010 ident: 10.1016/j.asoc.2020.106716_b39 article-title: A new fast prototype selection method based on clustering publication-title: Pattern Anal. Appl. doi: 10.1007/s10044-008-0142-x – volume: 190 year: 2020 ident: 10.1016/j.asoc.2020.106716_b48 article-title: NearCount: Selecting critical instances based on the cited counts of nearest neighbors publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2019.105196 – start-page: 37 year: 2010 ident: 10.1016/j.asoc.2020.106716_b49 article-title: Support vectors selection for supervised learning using an ensemble approach – start-page: 286 year: 2018 ident: 10.1016/j.asoc.2020.106716_b54 article-title: Efficient instance selection based on spatial abstraction – volume: 33 start-page: 1189 issue: 6 year: 2011 ident: 10.1016/j.asoc.2020.106716_b47 article-title: Selecting critical patterns based on local geometrical and statistical information publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2010.188 – start-page: 233 year: 2010 ident: 10.1016/j.asoc.2020.106716_b10 article-title: Tree decomposition for large-scale SVM problems – ident: 10.1016/j.asoc.2020.106716_b2 doi: 10.1109/IGARSS.2015.7326952 – volume: 104 start-page: 1 issue: 1 year: 2006 ident: 10.1016/j.asoc.2020.106716_b9 article-title: Training set size requirements for the classification of a specific class publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2006.03.004 – start-page: 2353 year: 2017 ident: 10.1016/j.asoc.2020.106716_b16 article-title: LSIS: Large scale instance selection algorithm for big data – volume: 13 start-page: 21 issue: 1 year: 1967 ident: 10.1016/j.asoc.2020.106716_b84 article-title: Nearest neighbor pattern classification publication-title: IEEE Trans. Inform. Theory doi: 10.1109/TIT.1967.1053964 – start-page: 219 year: 2000 ident: 10.1016/j.asoc.2020.106716_b13 article-title: Scaling-up support vector machines using boosting algorithm – ident: 10.1016/j.asoc.2020.106716_b56 – start-page: 323 year: 2005 ident: 10.1016/j.asoc.2020.106716_b58 – volume: 54 start-page: 22 year: 2013 ident: 10.1016/j.asoc.2020.106716_b69 article-title: Repeated double cross-validation for choosing a single solution in evolutionary multi-objective fuzzy classifier design publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2013.09.023 – start-page: 3199 year: 2003 ident: 10.1016/j.asoc.2020.106716_b43 article-title: Extract candidates of support vector from training set – volume: 20 start-page: 273 issue: 3 year: 1995 ident: 10.1016/j.asoc.2020.106716_b1 article-title: Support-vector networks publication-title: Mach. Learn. doi: 10.1023/A:1022627411411 – start-page: 306 year: 2003 ident: 10.1016/j.asoc.2020.106716_b36 article-title: Classifying large data sets using SVMs with hierarchical clusters |
| SSID | ssj0016928 |
| Score | 2.4086704 |
| Snippet | Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as... |
| SourceID | swepub crossref elsevier |
| SourceType | Open Access Repository Enrichment Source Index Database Publisher |
| StartPage | 106716 |
| SubjectTerms | Big data Building extraction Computerized Image Processing Data reduction Datoriserad bildbehandling Instance selection Support vector machines |
| Title | A fast instance selection method for support vector machines in building extraction |
| URI | https://dx.doi.org/10.1016/j.asoc.2020.106716 https://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-34022 https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425980 |
| Volume | 97 |
| WOSCitedRecordID | wos000603366700004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-9681 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016928 issn: 1568-4946 databaseCode: AIEXJ dateStart: 20010601 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELZgy4EL5SlKAfkApyir2HnZxwiKgEOF1IL2ZjmO027VTVebpOrPZ_xKlyJKOXCJrMhOIn-TyefJeD6E3sFXqElpXcdaa1ig1ITFMidNDFykzkqV8KR1YhPl4SFbLPg3Lw_WWzmBsuvY1RVf_1eo4RyAbbbO_gPc00XhBLQBdDgC7HC8E_BV1MreFP43vM8UkLVKNwZlpxZtEwv7cW2Id3Rpg_bRyqZU2tysqPZC2RG47Y3b9rDNYANt7cF_24T0cQhfP2M3YGFWJAqcxalcrWQzhXD0cu0yAo4G3Xqb9OEGup264T1kweKM-7ihd6EuxfY3b-wCA2dzCYY2Nxebm4J15Ebp66nK9cflj0pcbE7EOApwJZwl99EOLaExQzvVl4PF1-n_UMGtau70MKbNShrzghG_Ncpl8d289R_px3adWMstjh-jR35RgCsH5hN0T3dP0W4Q3MDe_z5DRxU22OKALZ6wxQ5bDNhijy122OKALYzCAVt8je1z9P3TwfGHz7FXxYhVRtgQq1SSMi1kqwhVRZHIJmkL1WgF1E4mqmA1S1MJy0Qu85ICP2G64GXCqWJNWeskfYFm3UWnXyKcwbBUZjU1KgLAFGtOJVVEyZxJljdqD5EwWUL5kvFGueRchNzAM2EmWJgJFm6C91A0jVm7gim39s4DBsJTPkflBNjQrePeO8Cme_xiQKfLE5FmQE__0m8ytFd37LePHl6_FK_RbNiM-g16oC6HZb956630Jyw_k-c |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+fast+instance+selection+method+for+support+vector+machines+in+building+extraction&rft.jtitle=Applied+soft+computing&rft.au=Aslani%2C+Mohammad&rft.au=Seipel%2C+Stefan&rft.date=2020-12-01&rft.issn=1568-4946&rft.volume=97&rft_id=info:doi/10.1016%2Fj.asoc.2020.106716&rft.externalDocID=oai_DiVA_org_uu_425980 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1568-4946&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1568-4946&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1568-4946&client=summon |