Bagging Supervised Autoencoder Classifier for credit scoring
Automatic credit scoring, a crucial risk management tool for banks and financial institutes, has attracted much attention in the past few decades. As such, various approaches have been developed to accurately and efficiently estimate defaults in loan applicants and seamlessly improve and facilitate...
Saved in:
| Published in: | Expert systems with applications Vol. 213; p. 118991 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.03.2023
|
| Subjects: | |
| ISSN: | 0957-4174, 1873-6793 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Automatic credit scoring, a crucial risk management tool for banks and financial institutes, has attracted much attention in the past few decades. As such, various approaches have been developed to accurately and efficiently estimate defaults in loan applicants and seamlessly improve and facilitate decision-making in the lending process. However, the imbalanced nature of credit scoring datasets, as well as the heterogeneous nature of features in credit scoring task pose many challenges in developing and implementing effective credit scoring models, targeting the generalization power of classification models on unseen data. To mitigate these challenges, in this paper, we propose the Bagging Supervised Autoencoder Classifier (BSAC). BSAC is a learning model which simultaneously leverages the superior power of supervised autoencoders and representation learning in classification, as well as the Bagging mechanism to handle the irregularities in feature space. Supervised autoencoder has been exploited to learn an optimal latent space from heterogeneous features and perform classification on top of the learned latent space. In particular, the Bagging mechanism has been employed in the learning process to construct various samples of original data to tackle the problem that arises from imbalanced data and irregularities of features in latent space. Extensive experiments on various real-world and benchmark datasets validate the superiority and robustness of the proposed method in predicting the outcome of loan applications.
•A novel credit scoring model using representation, ensemble, and multi-task learning.•In BSAC, the learned representations are guided by the label information of samples.•BSAC outperforms state-of-art baseline models in credit scoring imbalanced data.•BSAC performs significantly better than the best base classifier in the pool.•The model shows a balanced performance in classifying positive and negative samples. |
|---|---|
| AbstractList | Automatic credit scoring, a crucial risk management tool for banks and financial institutes, has attracted much attention in the past few decades. As such, various approaches have been developed to accurately and efficiently estimate defaults in loan applicants and seamlessly improve and facilitate decision-making in the lending process. However, the imbalanced nature of credit scoring datasets, as well as the heterogeneous nature of features in credit scoring task pose many challenges in developing and implementing effective credit scoring models, targeting the generalization power of classification models on unseen data. To mitigate these challenges, in this paper, we propose the Bagging Supervised Autoencoder Classifier (BSAC). BSAC is a learning model which simultaneously leverages the superior power of supervised autoencoders and representation learning in classification, as well as the Bagging mechanism to handle the irregularities in feature space. Supervised autoencoder has been exploited to learn an optimal latent space from heterogeneous features and perform classification on top of the learned latent space. In particular, the Bagging mechanism has been employed in the learning process to construct various samples of original data to tackle the problem that arises from imbalanced data and irregularities of features in latent space. Extensive experiments on various real-world and benchmark datasets validate the superiority and robustness of the proposed method in predicting the outcome of loan applications.
•A novel credit scoring model using representation, ensemble, and multi-task learning.•In BSAC, the learned representations are guided by the label information of samples.•BSAC outperforms state-of-art baseline models in credit scoring imbalanced data.•BSAC performs significantly better than the best base classifier in the pool.•The model shows a balanced performance in classifying positive and negative samples. |
| ArticleNumber | 118991 |
| Author | Abdoli, Mahsan Shahrabi, Jamal Akbari, Mohammad |
| Author_xml | – sequence: 1 givenname: Mahsan surname: Abdoli fullname: Abdoli, Mahsan email: m.abdoli@aut.ac.ir organization: Department of Industrial Engineering and Management Systems, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran – sequence: 2 givenname: Mohammad orcidid: 0000-0002-3321-5775 surname: Akbari fullname: Akbari, Mohammad email: akbari.ma@aut.ac.ir organization: Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran – sequence: 3 givenname: Jamal surname: Shahrabi fullname: Shahrabi, Jamal email: jamalshahrabi@aut.ac.ir organization: Department of Industrial Engineering and Management Systems, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran |
| BookMark | eNp9kMFKAzEURYNUsK3-gKv5gRlfJkkzgW5q0SoUXKjrkEnelJQ6Kcm04t-bUlcuunpvcy6cMyGjPvRIyD2FigKdPWwrTN-mqqGuK0obpegVGdNGsnImFRuRMSghS04lvyGTlLYAVALIMZk_ms3G95vi_bDHePQJXbE4DAF7GxzGYrkzKfnO57cLsbARnR-KZEPM0C257swu4d3fnZLP56eP5Uu5flu9Lhfr0jKAoawVCOHQNS00xiCgkrLtKCqgLZOqnbXYYS0t50xw4QTHBpFyqhiDVgrOpqQ579oYUorYaesHM_jQD9H4naagTxX0Vp8q6FMFfa6Q0fofuo_-y8Sfy9D8DGGWOmZ3nazPRbJ7RDtoF_wl_BcX3nh3 |
| CitedBy_id | crossref_primary_10_1016_j_eswa_2025_126448 crossref_primary_10_1109_JIOT_2024_3411695 crossref_primary_10_3390_app13148409 crossref_primary_10_1109_TNSM_2023_3292269 crossref_primary_10_3390_app13064006 crossref_primary_10_1016_j_eswa_2024_124525 crossref_primary_10_2166_wst_2024_052 crossref_primary_10_3390_electronics12112485 crossref_primary_10_1007_s00521_024_10452_3 crossref_primary_10_29407_ja_v9i1_23670 crossref_primary_10_1109_TII_2024_3393491 crossref_primary_10_1016_j_engappai_2023_106911 crossref_primary_10_1142_S2424786325500124 crossref_primary_10_1007_s11831_025_10260_5 crossref_primary_10_1007_s10115_024_02129_z crossref_primary_10_1108_BPMJ_09_2024_0886 crossref_primary_10_2139_ssrn_5059403 crossref_primary_10_1002_for_3033 crossref_primary_10_1007_s10479_024_06369_8 crossref_primary_10_3233_JIFS_230825 crossref_primary_10_3390_app13158701 crossref_primary_10_1016_j_eswa_2024_124072 crossref_primary_10_1002_for_70004 crossref_primary_10_1016_j_eswa_2023_121484 crossref_primary_10_1007_s10614_025_10893_5 |
| Cites_doi | 10.1007/s13748-020-00211-5 10.1371/journal.pone.0139427 10.1057/palgrave.jors.2601545 10.1080/00036846.2014.962222 10.1016/j.eswa.2015.02.001 10.1109/TSMCC.2011.2161285 10.1109/29.45535 10.1016/j.asoc.2018.01.021 10.1080/07350015.1983.10509329 10.1109/34.667881 10.1016/j.neucom.2018.07.070 10.1016/j.asoc.2021.107871 10.1023/A:1018054314350 10.1016/j.dss.2019.03.011 10.1016/j.asoc.2016.02.022 10.1186/s40537-014-0007-7 10.1145/3194452.3194456 10.2174/2666255813999200819164013 10.1016/j.asoc.2020.106852 10.1016/j.knosys.2019.105118 10.1016/j.dss.2019.01.002 10.1613/jair.953 10.1016/j.eswa.2016.12.035 10.1016/j.inffus.2018.07.004 10.1016/j.eswa.2019.05.042 10.1016/j.elerap.2017.06.004 10.1016/j.ejor.2015.05.030 10.1016/j.eswa.2020.113696 10.1016/j.eswa.2021.116034 10.1016/j.jfranklin.2019.01.046 10.1016/j.asoc.2018.04.049 10.1016/j.asoc.2020.106263 10.1016/j.eswa.2018.01.012 10.1016/j.eswa.2019.112918 10.1016/j.eswa.2010.06.048 10.1145/2939672.2939785 10.1109/TFUZZ.2010.2042721 10.1016/j.engappai.2019.103292 10.1016/j.eswa.2011.09.059 10.1016/j.csda.2010.06.014 10.1016/j.ins.2017.10.017 10.1023/A:1007379606734 10.1109/TSMCA.2009.2029559 10.1016/j.eswa.2015.04.042 10.1016/j.eswa.2017.10.022 10.1109/TSMCA.2010.2084081 10.1109/ACCESS.2021.3083490 10.1016/j.ejor.2017.02.037 10.1016/j.eswa.2020.114020 10.1016/j.eswa.2011.09.033 10.1109/ACCESS.2019.2922676 |
| ContentType | Journal Article |
| Copyright | 2022 Elsevier Ltd |
| Copyright_xml | – notice: 2022 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.eswa.2022.118991 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1873-6793 |
| ExternalDocumentID | 10_1016_j_eswa_2022_118991 S0957417422020097 |
| GroupedDBID | --K --M .DC .~1 0R~ 13V 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AAXUO AAYFN ABBOA ABFNM ABMAC ABMVD ABUCO ABYKQ ACDAQ ACGFS ACHRH ACNTT ACRLP ACZNC ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGJBL AGUBO AGUMN AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALEQD ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM AXJTR BJAXD BKOJK BLXMC BNSAS CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W JJJVA KOM LG9 LY1 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ SDF SDG SDP SDS SES SEW SPC SPCBC SSB SSD SSL SST SSV SSZ T5K TN5 ~G- 29G 9DU AAAKG AAQXK AATTM AAXKI AAYWO AAYXX ABJNI ABKBG ABUFD ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB G-2 HLZ HVGLF HZ~ R2- SBC SET WUQ XPP ZMT ~HD |
| ID | FETCH-LOGICAL-c300t-29055ded8b08aae0e977bf1e901b379b6befe27c443545d54e8ee1419330b7543 |
| ISICitedReferencesCount | 29 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000878295700009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0957-4174 |
| IngestDate | Tue Nov 18 21:07:07 EST 2025 Sat Nov 29 07:06:42 EST 2025 Fri Feb 23 02:39:27 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Imbalanced data Ensemble learning Autoencoder Multi-task learning Credit scoring |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-29055ded8b08aae0e977bf1e901b379b6befe27c443545d54e8ee1419330b7543 |
| ORCID | 0000-0002-3321-5775 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_eswa_2022_118991 crossref_primary_10_1016_j_eswa_2022_118991 elsevier_sciencedirect_doi_10_1016_j_eswa_2022_118991 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-03-01 2023-03-00 |
| PublicationDateYYYYMMDD | 2023-03-01 |
| PublicationDate_xml | – month: 03 year: 2023 text: 2023-03-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | Expert systems with applications |
| PublicationYear | 2023 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Chawla, Bowyer, Hall, Kegelmeyer (b11) 2002; 16 Yotsawat, Wattuya, Srivihok (b63) 2021; 9 Neagoe, Ciotec, Cucu (b42) 2018 He, Zhang, Zhang (b26) 2018; 98 Breiman (b7) 1996; 24 Brown, Mues (b8) 2012; 39 Farajian, Adibi (b19) 2020; 9 Goodfellow, Bengio, Courville (b23) 2016 Baesens, Van Gestel, Viaene, Stepanova, Suykens, Vanthienen (b1) 2003; 54 Feng, Xiao, Zhong, Qiu, Dong (b20) 2018; 65 Maleki, Motevallian, Hosseini, Sabokrou, Maleki (b36) 2021 Bengio, Courville, Vincent (b5) 2012 Bhatore, Mohan, Reddy (b6) 2020 Chen, Wang, Liu (b13) 2019 Galar, Fernandez, Barrenechea, Bustince, Herrera (b21) 2011; 42 Ruder (b46) 2017 Serrano-Cinca, Gutiérrez-Nieto, López-Palacios (b48) 2015; 10 Bahnsen, Aouada, Ottersten (b2) 2015; 42 Xiao, Zhou, Zhong, Xie, Gu, Liu (b61) 2020; 189 Morgan, Bourlard (b40) 1989; 2 Moreno-Barea, Jerez, Franco (b39) 2020; 161 Sun, Lang, Fujita, Li (b51) 2018; 425 Wang, Hao, Ma, Jiang (b55) 2011; 38 Veeramanikandan, Jeyakarthic (b53) 2021; 14 Malekipirbazari, Aksakalli (b37) 2015; 42 Yang, Qiao, Huang, Wang, Wang (b62) 2021; 113 Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Tran, Duong, Ho (b52) 2016 Caruana (b10) 1997; 28 Khoshgoftaar, Van Hulse, Napolitano (b27) 2010; 41 Liu, Fan, Xia (b33) 2022; 189 Le, Patterson, White (b30) 2018; 31 Prechelt (b44) 1998 Guo, He, Huang (b24) 2019; 7 (pp. 62–65). Dastile, Celik, Potsane (b14) 2020 Carta, Ferreira, Recupero, Saia, Saia (b9) 2020; 87 Reichert, Cho, Wagner (b45) 1983; 1 Bastani, Asgari, Namavari (b3) 2019; 134 Haixiang, Yijing, Shang, Mingyun, Yuanyue, Bing (b25) 2017; 73 Najafabadi, Villanustre, Khoshgoftaar, Seliya, Wald, Muharemagic (b41) 2015; 2 Xia, Liu, Da, Xie (b57) 2018; 93 Xiao, Xiao, Wang (b59) 2016; 43 Srivastava, Hinton, Krizhevsky, Sutskever, Salakhutdinov (b50) 2014; 15 Mancisidor, Kampffmeyer, Aas, Jenssen (b38) 2021; 164 Wong, Seng, Wong (b56) 2020; 141 Waibel, Sawai, Shikano (b54) 1989; 37 Papouskova, Hajek (b43) 2019; 118 Maalouf, Trafalis (b34) 2011; 55 Emekter, Tu, Jirasakuldech, Lu (b17) 2015; 47 Yu, Zhou, Tang, Chen (b64) 2018; 69 Kittler, Hatef, Duin, Matas (b28) 1998; 20 Kozodoi, Lessmann, Papakonstantinou, Gatsoulis, Baesens (b29) 2019; 120 Lessmann, Baesens, Seow, Thomas (b32) 2015; 247 Batuwita, Palade (b4) 2010; 18 Maldonado, Pérez, Bravo (b35) 2017; 261 Duan (b15) 2019; 356 Duin (b16) 2002; 2 Seiffert, Khoshgoftaar, Van Hulse, Napolitano (b47) 2009; 40 Lei, Xie, Zhong, Dai, Yang, Shen (b31) 2020 (pp. 785–794). Fan, Q., & Yang, J. (2018). A denoising autoencoder approach for credit risk analysis. In Shen, Zhao, Kou, Alsaadi (b49) 2021 Zhang, He, Zhang (b65) 2018; 316 García, Marqués, Sánchez (b22) 2019; 47 Xia, Liu, Liu (b58) 2017; 24 Xiao, Xie, He, Jiang (b60) 2012; 39 Bahnsen (10.1016/j.eswa.2022.118991_b2) 2015; 42 Kittler (10.1016/j.eswa.2022.118991_b28) 1998; 20 Xia (10.1016/j.eswa.2022.118991_b57) 2018; 93 Yang (10.1016/j.eswa.2022.118991_b62) 2021; 113 Maldonado (10.1016/j.eswa.2022.118991_b35) 2017; 261 Xiao (10.1016/j.eswa.2022.118991_b59) 2016; 43 Tran (10.1016/j.eswa.2022.118991_b52) 2016 Feng (10.1016/j.eswa.2022.118991_b20) 2018; 65 10.1016/j.eswa.2022.118991_b18 Seiffert (10.1016/j.eswa.2022.118991_b47) 2009; 40 Srivastava (10.1016/j.eswa.2022.118991_b50) 2014; 15 Bhatore (10.1016/j.eswa.2022.118991_b6) 2020 Wang (10.1016/j.eswa.2022.118991_b55) 2011; 38 Duan (10.1016/j.eswa.2022.118991_b15) 2019; 356 Xiao (10.1016/j.eswa.2022.118991_b61) 2020; 189 Galar (10.1016/j.eswa.2022.118991_b21) 2011; 42 Xia (10.1016/j.eswa.2022.118991_b58) 2017; 24 Batuwita (10.1016/j.eswa.2022.118991_b4) 2010; 18 Brown (10.1016/j.eswa.2022.118991_b8) 2012; 39 Maalouf (10.1016/j.eswa.2022.118991_b34) 2011; 55 Moreno-Barea (10.1016/j.eswa.2022.118991_b39) 2020; 161 10.1016/j.eswa.2022.118991_b12 Morgan (10.1016/j.eswa.2022.118991_b40) 1989; 2 Xiao (10.1016/j.eswa.2022.118991_b60) 2012; 39 Shen (10.1016/j.eswa.2022.118991_b49) 2021 Farajian (10.1016/j.eswa.2022.118991_b19) 2020; 9 Haixiang (10.1016/j.eswa.2022.118991_b25) 2017; 73 Ruder (10.1016/j.eswa.2022.118991_b46) 2017 Zhang (10.1016/j.eswa.2022.118991_b65) 2018; 316 Dastile (10.1016/j.eswa.2022.118991_b14) 2020 Veeramanikandan (10.1016/j.eswa.2022.118991_b53) 2021; 14 Caruana (10.1016/j.eswa.2022.118991_b10) 1997; 28 Guo (10.1016/j.eswa.2022.118991_b24) 2019; 7 Reichert (10.1016/j.eswa.2022.118991_b45) 1983; 1 Wong (10.1016/j.eswa.2022.118991_b56) 2020; 141 García (10.1016/j.eswa.2022.118991_b22) 2019; 47 Waibel (10.1016/j.eswa.2022.118991_b54) 1989; 37 He (10.1016/j.eswa.2022.118991_b26) 2018; 98 Chen (10.1016/j.eswa.2022.118991_b13) 2019 Neagoe (10.1016/j.eswa.2022.118991_b42) 2018 Baesens (10.1016/j.eswa.2022.118991_b1) 2003; 54 Prechelt (10.1016/j.eswa.2022.118991_b44) 1998 Le (10.1016/j.eswa.2022.118991_b30) 2018; 31 Najafabadi (10.1016/j.eswa.2022.118991_b41) 2015; 2 Emekter (10.1016/j.eswa.2022.118991_b17) 2015; 47 Lessmann (10.1016/j.eswa.2022.118991_b32) 2015; 247 Goodfellow (10.1016/j.eswa.2022.118991_b23) 2016 Bastani (10.1016/j.eswa.2022.118991_b3) 2019; 134 Sun (10.1016/j.eswa.2022.118991_b51) 2018; 425 Khoshgoftaar (10.1016/j.eswa.2022.118991_b27) 2010; 41 Lei (10.1016/j.eswa.2022.118991_b31) 2020 Mancisidor (10.1016/j.eswa.2022.118991_b38) 2021; 164 Serrano-Cinca (10.1016/j.eswa.2022.118991_b48) 2015; 10 Duin (10.1016/j.eswa.2022.118991_b16) 2002; 2 Carta (10.1016/j.eswa.2022.118991_b9) 2020; 87 Maleki (10.1016/j.eswa.2022.118991_b36) 2021 Malekipirbazari (10.1016/j.eswa.2022.118991_b37) 2015; 42 Bengio (10.1016/j.eswa.2022.118991_b5) 2012 Yotsawat (10.1016/j.eswa.2022.118991_b63) 2021; 9 Breiman (10.1016/j.eswa.2022.118991_b7) 1996; 24 Papouskova (10.1016/j.eswa.2022.118991_b43) 2019; 118 Kozodoi (10.1016/j.eswa.2022.118991_b29) 2019; 120 Yu (10.1016/j.eswa.2022.118991_b64) 2018; 69 Chawla (10.1016/j.eswa.2022.118991_b11) 2002; 16 Liu (10.1016/j.eswa.2022.118991_b33) 2022; 189 |
| References_xml | – volume: 247 start-page: 124 year: 2015 end-page: 136 ident: b32 article-title: Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research publication-title: European Journal of Operational Research – volume: 47 start-page: 88 year: 2019 end-page: 101 ident: b22 article-title: Exploring the synergetic effects of sample types on the performance of ensembles for credit risk and corporate bankruptcy prediction publication-title: Information Fusion – reference: . (pp. 62–65). – volume: 42 start-page: 4621 year: 2015 end-page: 4631 ident: b37 article-title: Risk assessment in social lending via random forests publication-title: Expert Systems with Applications – volume: 2 start-page: 765 year: 2002 end-page: 770 ident: b16 article-title: The combining classifier: To train or not to train? publication-title: Object recognition supported by user interaction for service robots – start-page: 182 year: 2021 end-page: 187 ident: b36 article-title: Improvement of credit scoring by lstm autoencoder model publication-title: 2021 11th International Conference on Computer Engineering and Knowledge – volume: 7 start-page: 78549 year: 2019 end-page: 78559 ident: b24 article-title: A multi-stage self-adaptive classifier ensemble model with application in credit scoring publication-title: IEEE Access – volume: 2 start-page: 630 year: 1989 end-page: 637 ident: b40 article-title: Generalization and parameter estimation in feedforward nets: Some experiments publication-title: Advances in Neural Information Processing Systems – start-page: 1 year: 2020 end-page: 28 ident: b6 article-title: Machine learning techniques for credit risk evaluation: A systematic literature review publication-title: Journal of Banking and Financial Technology – volume: 42 start-page: 463 year: 2011 end-page: 484 ident: b21 article-title: A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches publication-title: IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) – year: 2017 ident: b46 article-title: An overview of multi-task learning in deep neural networks – volume: 189 year: 2020 ident: b61 article-title: Cost-sensitive semi-supervised selective ensemble model for customer credit scoring publication-title: Knowledge-Based Systems – volume: 161 year: 2020 ident: b39 article-title: Improving classification accuracy using data augmentation on small data sets publication-title: Expert Systems with Applications – volume: 356 start-page: 4716 year: 2019 end-page: 4731 ident: b15 article-title: Financial system modeling using deep neural networks (DNNs) for effective risk assessment and prediction publication-title: Journal of the Franklin Institute – year: 2021 ident: b49 article-title: A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique publication-title: Applied Soft Computing – volume: 65 start-page: 139 year: 2018 end-page: 151 ident: b20 article-title: Dynamic ensemble classification for credit scoring using soft probability publication-title: Applied Soft Computing – volume: 9 start-page: 263 year: 2020 end-page: 274 ident: b19 article-title: DMRAE: Discriminative manifold regularized auto-encoder for sparse and robust feature learning publication-title: Progress in Artificial Intelligence – volume: 37 start-page: 1888 year: 1989 end-page: 1898 ident: b54 article-title: Modularity and scaling in large phonemic neural networks publication-title: IEEE Transactions on Acoustics, Speech and Signal Processing – volume: 47 start-page: 54 year: 2015 end-page: 70 ident: b17 article-title: Evaluating credit risk and loan performance in online peer-to-peer (P2P) lending publication-title: Applied Economics – reference: . (pp. 785–794). – volume: 38 start-page: 223 year: 2011 end-page: 230 ident: b55 article-title: A comparative assessment of ensemble learning for credit scoring publication-title: Expert Systems with Applications – volume: 39 start-page: 3668 year: 2012 end-page: 3675 ident: b60 article-title: Dynamic classifier ensemble model for customer classification with imbalanced class distribution publication-title: Expert Systems with Applications – volume: 39 start-page: 3446 year: 2012 end-page: 3453 ident: b8 article-title: An experimental comparison of classification algorithms for imbalanced credit scoring data sets publication-title: Expert Systems with Applications – volume: 69 start-page: 192 year: 2018 end-page: 202 ident: b64 article-title: A DBN-based resampling SVM ensemble learning paradigm for credit classification with imbalanced data publication-title: Applied Soft Computing – volume: 42 start-page: 6609 year: 2015 end-page: 6619 ident: b2 article-title: Example-dependent cost-sensitive decision trees publication-title: Expert Systems with Applications – volume: 2 start-page: 1 year: 2015 end-page: 24 ident: b41 article-title: Deep learning applications and challenges in big data analytics publication-title: Journal of Big Data – volume: 164 start-page: 114020 year: 2021 ident: b38 article-title: Learning latent representations of bank customers with the variational autoencoder publication-title: Expert Systems with Applications – volume: 24 start-page: 123 year: 1996 end-page: 140 ident: b7 article-title: Bagging predictors publication-title: Machine Learning – volume: 16 start-page: 321 year: 2002 end-page: 357 ident: b11 article-title: SMOTE: Synthetic minority over-sampling technique publication-title: Journal of Artificial Intelligence Research – start-page: 1 year: 2020 end-page: 12 ident: b31 article-title: Generative adversarial fusion network for class imbalance credit scoring publication-title: Neural Computing and Applications – volume: 40 start-page: 185 year: 2009 end-page: 197 ident: b47 article-title: RUSboost: A hybrid approach to alleviating class imbalance publication-title: IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans – reference: Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In – volume: 113 year: 2021 ident: b62 article-title: An automatic credit scoring strategy (ACSS) using memetic evolutionary algorithm and neural architecture search publication-title: Applied Soft Computing – start-page: 55 year: 1998 end-page: 69 ident: b44 article-title: Early stopping-but when? publication-title: Neural networks: tricks of the trade – volume: 20 start-page: 226 year: 1998 end-page: 239 ident: b28 article-title: On combining classifiers publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – volume: 18 start-page: 558 year: 2010 end-page: 571 ident: b4 article-title: FSVM-CIL: Fuzzy support vector machines for class imbalance learning publication-title: IEEE Transactions on Fuzzy Systems – volume: 41 start-page: 552 year: 2010 end-page: 568 ident: b27 article-title: Comparing boosting and bagging techniques with noisy and imbalanced data publication-title: IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans – volume: 98 start-page: 105 year: 2018 end-page: 117 ident: b26 article-title: A novel ensemble method for credit scoring: Adaption of different imbalance ratios publication-title: Expert Systems with Applications – start-page: 4373 year: 2019 end-page: 4377 ident: b13 article-title: Credit risk prediction in peer-to-peer lending with ensemble learning framework publication-title: 2019 chinese control and decision conference – volume: 425 start-page: 76 year: 2018 end-page: 91 ident: b51 article-title: Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates publication-title: Information Sciences – volume: 134 start-page: 209 year: 2019 end-page: 224 ident: b3 article-title: Wide and deep learning for peer-to-peer lending publication-title: Expert Systems with Applications – reference: Fan, Q., & Yang, J. (2018). A denoising autoencoder approach for credit risk analysis. In – volume: 54 start-page: 627 year: 2003 end-page: 635 ident: b1 article-title: Benchmarking state-of-the-art classification algorithms for credit scoring publication-title: Journal of the Operational Research Society – volume: 73 start-page: 220 year: 2017 end-page: 239 ident: b25 article-title: Learning from class-imbalanced data: Review of methods and applications publication-title: Expert Systems with Applications – volume: 87 start-page: 103292 year: 2020 ident: b9 article-title: A combined entropy-based approach for a proactive credit scoring publication-title: Engineering Applications of Artificial Intelligence – volume: 10 year: 2015 ident: b48 article-title: Determinants of default in P2P lending publication-title: PLoS One – start-page: 145 year: 2016 end-page: 149 ident: b52 article-title: Credit scoring model: A combination of genetic programming and deep learning publication-title: 2016 future technologies conference – year: 2020 ident: b14 article-title: Statistical and machine learning models in credit scoring: A systematic literature survey publication-title: Applied Soft Computing – volume: 14 start-page: 2958 year: 2021 end-page: 2968 ident: b53 article-title: Parameter-tuned deep learning model for credit risk assessment and scoring applications publication-title: Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science) – volume: 1 start-page: 101 year: 1983 end-page: 114 ident: b45 article-title: An examination of the conceptual issues involved in developing credit-scoring models publication-title: Journal of Business & Economic Statistics – year: 2016 ident: b23 article-title: Deep learning – volume: 189 start-page: 116034 year: 2022 ident: b33 article-title: Credit scoring based on tree-enhanced gradient boosting decision trees publication-title: Expert Systems with Applications – volume: 15 start-page: 1929 year: 2014 end-page: 1958 ident: b50 article-title: Dropout: A simple way to prevent neural networks from overfitting publication-title: Journal of Machine Learning Research – volume: 24 start-page: 30 year: 2017 end-page: 49 ident: b58 article-title: Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending publication-title: Electronic Commerce Research and Applications – volume: 55 start-page: 168 year: 2011 end-page: 183 ident: b34 article-title: Robust weighted kernel logistic regression in imbalanced and rare events data publication-title: Computational Statistics & Data Analysis – volume: 316 start-page: 210 year: 2018 end-page: 221 ident: b65 article-title: Classifier selection and clustering with fuzzy assignment in ensemble model for credit scoring publication-title: Neurocomputing – volume: 31 start-page: 107 year: 2018 end-page: 117 ident: b30 article-title: Supervised autoencoders: Improving generalization performance with unsupervised regularizers publication-title: Advances in Neural Information Processing Systems – volume: 9 start-page: 78521 year: 2021 end-page: 78537 ident: b63 article-title: A novel method for credit scoring based on cost-sensitive neural network ensemble publication-title: IEEE Access – start-page: 2012 year: 2012 ident: b5 article-title: Unsupervised feature learning and deep learning: A review and new perspectives – volume: 28 start-page: 41 year: 1997 end-page: 75 ident: b10 article-title: Multitask learning publication-title: Machine Learning – volume: 93 start-page: 182 year: 2018 end-page: 199 ident: b57 article-title: A novel heterogeneous ensemble credit scoring model based on bstacking approach publication-title: Expert Systems with Applications – volume: 120 start-page: 106 year: 2019 end-page: 117 ident: b29 article-title: A multi-objective approach for profit-driven feature selection in credit scoring publication-title: Decision Support Systems – volume: 43 start-page: 73 year: 2016 end-page: 86 ident: b59 article-title: Ensemble classification based on supervised clustering for credit scoring publication-title: Applied Soft Computing – start-page: 201 year: 2018 end-page: 206 ident: b42 article-title: Deep convolutional neural networks versus multilayer perceptron for financial prediction publication-title: 2018 international conference on communications – volume: 141 year: 2020 ident: b56 article-title: Cost-sensitive ensemble of stacked denoising autoencoders for class imbalance problems in business domain publication-title: Expert Systems with Applications – volume: 261 start-page: 656 year: 2017 end-page: 665 ident: b35 article-title: Cost-based feature selection for support vector machines: An application in credit scoring publication-title: European Journal of Operational Research – volume: 118 start-page: 33 year: 2019 end-page: 45 ident: b43 article-title: Two-stage consumer credit risk modelling using heterogeneous ensemble learning publication-title: Decision Support Systems – volume: 9 start-page: 263 year: 2020 ident: 10.1016/j.eswa.2022.118991_b19 article-title: DMRAE: Discriminative manifold regularized auto-encoder for sparse and robust feature learning publication-title: Progress in Artificial Intelligence doi: 10.1007/s13748-020-00211-5 – volume: 10 year: 2015 ident: 10.1016/j.eswa.2022.118991_b48 article-title: Determinants of default in P2P lending publication-title: PLoS One doi: 10.1371/journal.pone.0139427 – volume: 54 start-page: 627 year: 2003 ident: 10.1016/j.eswa.2022.118991_b1 article-title: Benchmarking state-of-the-art classification algorithms for credit scoring publication-title: Journal of the Operational Research Society doi: 10.1057/palgrave.jors.2601545 – volume: 47 start-page: 54 year: 2015 ident: 10.1016/j.eswa.2022.118991_b17 article-title: Evaluating credit risk and loan performance in online peer-to-peer (P2P) lending publication-title: Applied Economics doi: 10.1080/00036846.2014.962222 – volume: 42 start-page: 4621 year: 2015 ident: 10.1016/j.eswa.2022.118991_b37 article-title: Risk assessment in social lending via random forests publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2015.02.001 – volume: 42 start-page: 463 year: 2011 ident: 10.1016/j.eswa.2022.118991_b21 article-title: A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches publication-title: IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) doi: 10.1109/TSMCC.2011.2161285 – volume: 37 start-page: 1888 year: 1989 ident: 10.1016/j.eswa.2022.118991_b54 article-title: Modularity and scaling in large phonemic neural networks publication-title: IEEE Transactions on Acoustics, Speech and Signal Processing doi: 10.1109/29.45535 – volume: 65 start-page: 139 year: 2018 ident: 10.1016/j.eswa.2022.118991_b20 article-title: Dynamic ensemble classification for credit scoring using soft probability publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2018.01.021 – volume: 1 start-page: 101 year: 1983 ident: 10.1016/j.eswa.2022.118991_b45 article-title: An examination of the conceptual issues involved in developing credit-scoring models publication-title: Journal of Business & Economic Statistics doi: 10.1080/07350015.1983.10509329 – volume: 20 start-page: 226 year: 1998 ident: 10.1016/j.eswa.2022.118991_b28 article-title: On combining classifiers publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/34.667881 – volume: 2 start-page: 765 year: 2002 ident: 10.1016/j.eswa.2022.118991_b16 article-title: The combining classifier: To train or not to train? – volume: 316 start-page: 210 year: 2018 ident: 10.1016/j.eswa.2022.118991_b65 article-title: Classifier selection and clustering with fuzzy assignment in ensemble model for credit scoring publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.07.070 – volume: 113 year: 2021 ident: 10.1016/j.eswa.2022.118991_b62 article-title: An automatic credit scoring strategy (ACSS) using memetic evolutionary algorithm and neural architecture search publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2021.107871 – volume: 24 start-page: 123 year: 1996 ident: 10.1016/j.eswa.2022.118991_b7 article-title: Bagging predictors publication-title: Machine Learning doi: 10.1023/A:1018054314350 – volume: 120 start-page: 106 year: 2019 ident: 10.1016/j.eswa.2022.118991_b29 article-title: A multi-objective approach for profit-driven feature selection in credit scoring publication-title: Decision Support Systems doi: 10.1016/j.dss.2019.03.011 – volume: 43 start-page: 73 year: 2016 ident: 10.1016/j.eswa.2022.118991_b59 article-title: Ensemble classification based on supervised clustering for credit scoring publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2016.02.022 – start-page: 145 year: 2016 ident: 10.1016/j.eswa.2022.118991_b52 article-title: Credit scoring model: A combination of genetic programming and deep learning – year: 2016 ident: 10.1016/j.eswa.2022.118991_b23 – volume: 2 start-page: 1 year: 2015 ident: 10.1016/j.eswa.2022.118991_b41 article-title: Deep learning applications and challenges in big data analytics publication-title: Journal of Big Data doi: 10.1186/s40537-014-0007-7 – ident: 10.1016/j.eswa.2022.118991_b18 doi: 10.1145/3194452.3194456 – volume: 14 start-page: 2958 year: 2021 ident: 10.1016/j.eswa.2022.118991_b53 article-title: Parameter-tuned deep learning model for credit risk assessment and scoring applications publication-title: Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science) doi: 10.2174/2666255813999200819164013 – year: 2021 ident: 10.1016/j.eswa.2022.118991_b49 article-title: A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2020.106852 – volume: 189 year: 2020 ident: 10.1016/j.eswa.2022.118991_b61 article-title: Cost-sensitive semi-supervised selective ensemble model for customer credit scoring publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2019.105118 – volume: 2 start-page: 630 year: 1989 ident: 10.1016/j.eswa.2022.118991_b40 article-title: Generalization and parameter estimation in feedforward nets: Some experiments publication-title: Advances in Neural Information Processing Systems – volume: 118 start-page: 33 year: 2019 ident: 10.1016/j.eswa.2022.118991_b43 article-title: Two-stage consumer credit risk modelling using heterogeneous ensemble learning publication-title: Decision Support Systems doi: 10.1016/j.dss.2019.01.002 – volume: 16 start-page: 321 year: 2002 ident: 10.1016/j.eswa.2022.118991_b11 article-title: SMOTE: Synthetic minority over-sampling technique publication-title: Journal of Artificial Intelligence Research doi: 10.1613/jair.953 – start-page: 4373 year: 2019 ident: 10.1016/j.eswa.2022.118991_b13 article-title: Credit risk prediction in peer-to-peer lending with ensemble learning framework – volume: 73 start-page: 220 year: 2017 ident: 10.1016/j.eswa.2022.118991_b25 article-title: Learning from class-imbalanced data: Review of methods and applications publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2016.12.035 – start-page: 1 year: 2020 ident: 10.1016/j.eswa.2022.118991_b6 article-title: Machine learning techniques for credit risk evaluation: A systematic literature review publication-title: Journal of Banking and Financial Technology – year: 2017 ident: 10.1016/j.eswa.2022.118991_b46 – volume: 47 start-page: 88 year: 2019 ident: 10.1016/j.eswa.2022.118991_b22 article-title: Exploring the synergetic effects of sample types on the performance of ensembles for credit risk and corporate bankruptcy prediction publication-title: Information Fusion doi: 10.1016/j.inffus.2018.07.004 – volume: 134 start-page: 209 year: 2019 ident: 10.1016/j.eswa.2022.118991_b3 article-title: Wide and deep learning for peer-to-peer lending publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2019.05.042 – volume: 15 start-page: 1929 year: 2014 ident: 10.1016/j.eswa.2022.118991_b50 article-title: Dropout: A simple way to prevent neural networks from overfitting publication-title: Journal of Machine Learning Research – volume: 24 start-page: 30 year: 2017 ident: 10.1016/j.eswa.2022.118991_b58 article-title: Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending publication-title: Electronic Commerce Research and Applications doi: 10.1016/j.elerap.2017.06.004 – volume: 247 start-page: 124 year: 2015 ident: 10.1016/j.eswa.2022.118991_b32 article-title: Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2015.05.030 – volume: 161 year: 2020 ident: 10.1016/j.eswa.2022.118991_b39 article-title: Improving classification accuracy using data augmentation on small data sets publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.113696 – volume: 189 start-page: 116034 year: 2022 ident: 10.1016/j.eswa.2022.118991_b33 article-title: Credit scoring based on tree-enhanced gradient boosting decision trees publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2021.116034 – start-page: 182 year: 2021 ident: 10.1016/j.eswa.2022.118991_b36 article-title: Improvement of credit scoring by lstm autoencoder model – volume: 356 start-page: 4716 year: 2019 ident: 10.1016/j.eswa.2022.118991_b15 article-title: Financial system modeling using deep neural networks (DNNs) for effective risk assessment and prediction publication-title: Journal of the Franklin Institute doi: 10.1016/j.jfranklin.2019.01.046 – volume: 31 start-page: 107 year: 2018 ident: 10.1016/j.eswa.2022.118991_b30 article-title: Supervised autoencoders: Improving generalization performance with unsupervised regularizers publication-title: Advances in Neural Information Processing Systems – volume: 69 start-page: 192 year: 2018 ident: 10.1016/j.eswa.2022.118991_b64 article-title: A DBN-based resampling SVM ensemble learning paradigm for credit classification with imbalanced data publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2018.04.049 – start-page: 55 year: 1998 ident: 10.1016/j.eswa.2022.118991_b44 article-title: Early stopping-but when? – year: 2020 ident: 10.1016/j.eswa.2022.118991_b14 article-title: Statistical and machine learning models in credit scoring: A systematic literature survey publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2020.106263 – volume: 98 start-page: 105 year: 2018 ident: 10.1016/j.eswa.2022.118991_b26 article-title: A novel ensemble method for credit scoring: Adaption of different imbalance ratios publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2018.01.012 – volume: 141 year: 2020 ident: 10.1016/j.eswa.2022.118991_b56 article-title: Cost-sensitive ensemble of stacked denoising autoencoders for class imbalance problems in business domain publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2019.112918 – volume: 38 start-page: 223 year: 2011 ident: 10.1016/j.eswa.2022.118991_b55 article-title: A comparative assessment of ensemble learning for credit scoring publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2010.06.048 – ident: 10.1016/j.eswa.2022.118991_b12 doi: 10.1145/2939672.2939785 – volume: 18 start-page: 558 year: 2010 ident: 10.1016/j.eswa.2022.118991_b4 article-title: FSVM-CIL: Fuzzy support vector machines for class imbalance learning publication-title: IEEE Transactions on Fuzzy Systems doi: 10.1109/TFUZZ.2010.2042721 – volume: 87 start-page: 103292 year: 2020 ident: 10.1016/j.eswa.2022.118991_b9 article-title: A combined entropy-based approach for a proactive credit scoring publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2019.103292 – volume: 39 start-page: 3668 year: 2012 ident: 10.1016/j.eswa.2022.118991_b60 article-title: Dynamic classifier ensemble model for customer classification with imbalanced class distribution publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2011.09.059 – start-page: 1 year: 2020 ident: 10.1016/j.eswa.2022.118991_b31 article-title: Generative adversarial fusion network for class imbalance credit scoring publication-title: Neural Computing and Applications – volume: 55 start-page: 168 year: 2011 ident: 10.1016/j.eswa.2022.118991_b34 article-title: Robust weighted kernel logistic regression in imbalanced and rare events data publication-title: Computational Statistics & Data Analysis doi: 10.1016/j.csda.2010.06.014 – volume: 425 start-page: 76 year: 2018 ident: 10.1016/j.eswa.2022.118991_b51 article-title: Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates publication-title: Information Sciences doi: 10.1016/j.ins.2017.10.017 – volume: 28 start-page: 41 year: 1997 ident: 10.1016/j.eswa.2022.118991_b10 article-title: Multitask learning publication-title: Machine Learning doi: 10.1023/A:1007379606734 – volume: 40 start-page: 185 year: 2009 ident: 10.1016/j.eswa.2022.118991_b47 article-title: RUSboost: A hybrid approach to alleviating class imbalance publication-title: IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans doi: 10.1109/TSMCA.2009.2029559 – volume: 42 start-page: 6609 year: 2015 ident: 10.1016/j.eswa.2022.118991_b2 article-title: Example-dependent cost-sensitive decision trees publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2015.04.042 – volume: 93 start-page: 182 year: 2018 ident: 10.1016/j.eswa.2022.118991_b57 article-title: A novel heterogeneous ensemble credit scoring model based on bstacking approach publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2017.10.022 – volume: 41 start-page: 552 year: 2010 ident: 10.1016/j.eswa.2022.118991_b27 article-title: Comparing boosting and bagging techniques with noisy and imbalanced data publication-title: IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans doi: 10.1109/TSMCA.2010.2084081 – volume: 9 start-page: 78521 year: 2021 ident: 10.1016/j.eswa.2022.118991_b63 article-title: A novel method for credit scoring based on cost-sensitive neural network ensemble publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3083490 – start-page: 2012 year: 2012 ident: 10.1016/j.eswa.2022.118991_b5 – volume: 261 start-page: 656 year: 2017 ident: 10.1016/j.eswa.2022.118991_b35 article-title: Cost-based feature selection for support vector machines: An application in credit scoring publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2017.02.037 – volume: 164 start-page: 114020 year: 2021 ident: 10.1016/j.eswa.2022.118991_b38 article-title: Learning latent representations of bank customers with the variational autoencoder publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.114020 – volume: 39 start-page: 3446 year: 2012 ident: 10.1016/j.eswa.2022.118991_b8 article-title: An experimental comparison of classification algorithms for imbalanced credit scoring data sets publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2011.09.033 – volume: 7 start-page: 78549 year: 2019 ident: 10.1016/j.eswa.2022.118991_b24 article-title: A multi-stage self-adaptive classifier ensemble model with application in credit scoring publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2922676 – start-page: 201 year: 2018 ident: 10.1016/j.eswa.2022.118991_b42 article-title: Deep convolutional neural networks versus multilayer perceptron for financial prediction |
| SSID | ssj0017007 |
| Score | 2.5858831 |
| Snippet | Automatic credit scoring, a crucial risk management tool for banks and financial institutes, has attracted much attention in the past few decades. As such,... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 118991 |
| SubjectTerms | Autoencoder Credit scoring Ensemble learning Imbalanced data Multi-task learning |
| Title | Bagging Supervised Autoencoder Classifier for credit scoring |
| URI | https://dx.doi.org/10.1016/j.eswa.2022.118991 |
| Volume | 213 |
| WOSCitedRecordID | wos000878295700009&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: 1873-6793 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017007 issn: 0957-4174 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1NT9wwELXo0kMv0EIr6Jd86A0FOY6NY6mXpaJqkUCVAGlvke043aUlu9pkW35-x7HzAS2oHLhEUZQ4Tp4zeR7PzEPoAycmtoTqyE2-IiYUi1JdwOdunFq3hj-kzBuxCXF6mk4m8ltQRa0aOQFRlun1tVw8KtRwDMB2qbMPgLtrFA7APoAOW4Adtv8F_KH63ggPna0Wzg5UwCjHq3ruCla6uhGNCuascEkmLsLQFQyd1XuVaQLxbvjpXRHkOpR6bpPgBsvd_cJRPvc51idqOgjxGf_Qyqexn8yn6upK5Z03Z6qmS6VnIUo3PFBwPdCkj73qfIgiYrGX2WnNKY2TgUGE-Yv0clx_2WrvNrjct9VvVwCK0v3-5JuFsW_9sLowwjZC7TJzbWSujcy38QStU8FlOkLr469Hk-NuYUkQn0Hf9jzkUfmQv9s9-TdXGfCP8-doI0wc8NgD_gKt2XILbbaiHDjY6G30MeCPe_zxAH_c448Bf-zxxwH_l-ji89H5py9RkMiITEJIHVFJOM9tnmqSKmWJBTqvi9gCy9OJkPpA28JSYRiwYsZzzmxqbcxi58bSgrPkFRqV89LuIHzArWRxbhR3yclUSe7YPCM6YXmhhdlFcfsyMhPqxzsZk5_Z3TDsor3umoWvnnLv2bx9x1ngf57XZTBk7rnu9YPu8gY968fyWzSqlyv7Dj01v-pZtXwfxssfu5Z6ww |
| 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=Bagging+Supervised+Autoencoder+Classifier+for+credit+scoring&rft.jtitle=Expert+systems+with+applications&rft.au=Abdoli%2C+Mahsan&rft.au=Akbari%2C+Mohammad&rft.au=Shahrabi%2C+Jamal&rft.date=2023-03-01&rft.issn=0957-4174&rft.volume=213&rft.spage=118991&rft_id=info:doi/10.1016%2Fj.eswa.2022.118991&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_eswa_2022_118991 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0957-4174&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0957-4174&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0957-4174&client=summon |