Grammar-based automatic programming for medical data classification: an experimental study
In a computational medical model, diagnosis is the classification of disease status in the terms of abnormal or positive , normal or negative or intermediate stages . Different Machine learning techniques such as artificial neural networks (ANNs) are extensively and successfully used in disease diag...
Uložené v:
| Vydané v: | The Artificial intelligence review Ročník 54; číslo 6; s. 4097 - 4135 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Dordrecht
Springer Netherlands
01.08.2021
Springer Springer Nature B.V |
| Predmet: | |
| ISSN: | 0269-2821, 1573-7462 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | In a computational medical model, diagnosis is the classification of disease status in the terms of
abnormal
or
positive
,
normal
or
negative
or
intermediate stages
. Different Machine learning techniques such as artificial neural networks (ANNs) are extensively and successfully used in disease diagnosis. However, there is no single classifier that can solve all classification problems. Selecting an optimal classifier for a problem is difficult, and it has become a relevant subject in the area. This paper focuses on grammar-based automatic programming (GAP) to build optimized discriminant functions for medical data classification in any arbitrary language. These techniques have an implicit power of automatic feature selection and feature extraction. This work carries out an in-depth investigation of the use of different GAP algorithms in the medical data classification problem. The objective is to identify the benefits and limitations of algorithms of this nature in the current problem. Classical classifiers were also considered for comparison purposes. Fourteen medical data sets were used in the experiments, and seven performance measures such as accuracy, sensitivity, specificity, precision, geometric-mean, F-measure, and false-positive rate are used to evaluate the performance of the produced classifier. The multiple criteria decision analysis (MCDA) demonstrates that GAP approaches are able to produce suitable classifiers for a given problem, and the GS performs better than other classical classifiers in medical data classification. |
|---|---|
| AbstractList | In a computational medical model, diagnosis is the classification of disease status in the terms of abnormal or positive, normal or negative or intermediate stages. Different Machine learning techniques such as artificial neural networks (ANNs) are extensively and successfully used in disease diagnosis. However, there is no single classifier that can solve all classification problems. Selecting an optimal classifier for a problem is difficult, and it has become a relevant subject in the area. This paper focuses on grammar-based automatic programming (GAP) to build optimized discriminant functions for medical data classification in any arbitrary language. These techniques have an implicit power of automatic feature selection and feature extraction. This work carries out an in-depth investigation of the use of different GAP algorithms in the medical data classification problem. The objective is to identify the benefits and limitations of algorithms of this nature in the current problem. Classical classifiers were also considered for comparison purposes. Fourteen medical data sets were used in the experiments, and seven performance measures such as accuracy, sensitivity, specificity, precision, geometric-mean, F-measure, and false-positive rate are used to evaluate the performance of the produced classifier. The multiple criteria decision analysis (MCDA) demonstrates that GAP approaches are able to produce suitable classifiers for a given problem, and the GS performs better than other classical classifiers in medical data classification. In a computational medical model, diagnosis is the classification of disease status in the terms of abnormal or positive , normal or negative or intermediate stages . Different Machine learning techniques such as artificial neural networks (ANNs) are extensively and successfully used in disease diagnosis. However, there is no single classifier that can solve all classification problems. Selecting an optimal classifier for a problem is difficult, and it has become a relevant subject in the area. This paper focuses on grammar-based automatic programming (GAP) to build optimized discriminant functions for medical data classification in any arbitrary language. These techniques have an implicit power of automatic feature selection and feature extraction. This work carries out an in-depth investigation of the use of different GAP algorithms in the medical data classification problem. The objective is to identify the benefits and limitations of algorithms of this nature in the current problem. Classical classifiers were also considered for comparison purposes. Fourteen medical data sets were used in the experiments, and seven performance measures such as accuracy, sensitivity, specificity, precision, geometric-mean, F-measure, and false-positive rate are used to evaluate the performance of the produced classifier. The multiple criteria decision analysis (MCDA) demonstrates that GAP approaches are able to produce suitable classifiers for a given problem, and the GS performs better than other classical classifiers in medical data classification. |
| Audience | Academic |
| Author | Galdino, João Victor Miranda, Péricles Si, Tapas Nascimento, André |
| Author_xml | – sequence: 1 givenname: Tapas orcidid: 0000-0001-8267-0304 surname: Si fullname: Si, Tapas email: c2.tapas@gmail.com organization: Department of Computer Science and Engineering, Bankura Unnayani Institute of Engineering – sequence: 2 givenname: Péricles surname: Miranda fullname: Miranda, Péricles organization: Departamento de Computação (DC), Universidade Federal Rural de Pernambuco (UFRPE) – sequence: 3 givenname: João Victor surname: Galdino fullname: Galdino, João Victor organization: Departamento de Computação (DC), Universidade Federal Rural de Pernambuco (UFRPE) – sequence: 4 givenname: André surname: Nascimento fullname: Nascimento, André organization: Departamento de Computação (DC), Universidade Federal Rural de Pernambuco (UFRPE) |
| BookMark | eNp9kE1r3DAQhkVJoJuPP5CToWelI9myrN5CaD4gkEtyyUWM5fGiYMtbSQvNv482LhRyCDoIRu-jmXlO2FFYAjF2IeBSAOifSUDTSg4SOBjTGG6-sY1Quua61I_YBmRruOyk-M5OUnoFACWbesNebiPOM0beY6Khwn1eZszeVbu4bA9PPmyrcYnVTIN3OFUDZqzchCn5sRSyX8KvCkNFf3cU_Uwhl1DK--HtjB2POCU6_3efsueb30_Xd_zh8fb--uqBu1p1mfe9JgftCKLvSWrUNYFxqpMKOyIURrfDaFrpGq07o0BKEtSSgk73AypXn7If679l5D97Stm-LvsYSksrlWpUWVWZkrpcU1ucyPowLjmiK2eg2btic_SlfqVFcSR12xagWwEXl5Qijdb5_LFvAf1kBdiDeruqt0W9_VBvD73kJ3RX1GB8-xqqVyiVcNhS_L_GF9Q7yvSZIA |
| CitedBy_id | crossref_primary_10_1038_s41598_025_86527_5 crossref_primary_10_1007_s11036_023_02237_0 crossref_primary_10_1016_j_eswa_2021_116423 crossref_primary_10_1007_s10044_022_01099_8 crossref_primary_10_1007_s11042_023_14329_w crossref_primary_10_1038_s41598_023_36300_3 crossref_primary_10_1002_cbdv_202201123 crossref_primary_10_1016_j_eswa_2022_117481 crossref_primary_10_1007_s11042_023_15144_z crossref_primary_10_1093_bfgp_elac025 crossref_primary_10_1038_s41598_023_48553_z |
| Cites_doi | 10.1142/S0219622012500095 10.1109/ROPEC.2014.7036349 10.1142/S0219622019500329 10.1007/s10852-007-9065-6 10.1007/978-1-4757-3157-6 10.1007/s10710-010-9109-y 10.1109/CIBCB.2006.330951 10.1016/j.neucom.2010.05.010 10.1016/j.cmpb.2015.12.008 10.1109/CEC.2013.6557604 10.1155/S1110865704309108 10.1155/2019/4182639 10.1109/4235.942529 10.1016/j.aci.2018.08.003 10.1109/CIISP.2007.369178 10.1109/CEC.2017.7969488 10.1016/j.dss.2006.12.011 10.1007/s10710-008-9059-9 10.1016/S0031-3203(02)00257-1 10.1186/1756-0381-3-8 10.1109/TSMCC.2009.2033566 10.1016/j.jss.2012.01.025 10.1016/j.measurement.2009.01.004 10.1007/978-1-4615-0447-4_4 10.1007/978-3-319-03753-0_39 10.1109/4235.910462 10.1109/RoboMech.2016.7813165 10.1109/TCYB.2015.2404806 10.1007/978-981-10-0448-3_4 10.1142/S0218126618501086 10.1109/ICMLC.2018.8526925 10.1016/j.eswa.2007.01.006 10.1007/s11047-006-9007-7 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021 COPYRIGHT 2021 Springer Copyright Springer Nature B.V. Aug 2021 |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021 – notice: COPYRIGHT 2021 Springer – notice: Copyright Springer Nature B.V. Aug 2021 |
| DBID | AAYXX CITATION 3V. 7SC 7T9 7WY 7WZ 7XB 87Z 8AL 8AO 8FD 8FE 8FG 8FK 8FL ABUWG AFKRA ALSLI ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU CNYFK DWQXO E3H F2A FRNLG F~G GNUQQ HCIFZ JQ2 K60 K6~ K7- L.- L7M L~C L~D M0C M0N M1O P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI PRINS PRQQA PSYQQ Q9U |
| DOI | 10.1007/s10462-020-09949-9 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts Linguistics and Language Behavior Abstracts (LLBA) ABI商业信息数据库 ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Collection Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni Edition) ProQuest Central (Alumni) ProQuest Central UK/Ireland Social Science Premium Collection Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Business Premium Collection Technology Collection ProQuest One Community College Library & Information Science Collection ProQuest Central Korea Library & Information Sciences Abstracts (LISA) Library & Information Science Abstracts (LISA) Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database ABI/INFORM Professional Advanced Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global Computing Database Library Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Business (UW System Shared) ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China One Social Sciences One Psychology ProQuest Central Basic |
| DatabaseTitle | CrossRef ProQuest Business Collection (Alumni Edition) ProQuest One Psychology Computer Science Database ProQuest Central Student Library and Information Science Abstracts (LISA) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China ABI/INFORM Complete ProQuest One Applied & Life Sciences Library & Information Science Collection ProQuest Central (New) Advanced Technologies & Aerospace Collection Business Premium Collection Social Science Premium Collection ABI/INFORM Global ProQuest One Academic Eastern Edition Linguistics and Language Behavior Abstracts (LLBA) ProQuest Technology Collection ProQuest Business Collection ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ABI/INFORM Global (Corporate) ProQuest One Business Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Pharma Collection ProQuest Central ABI/INFORM Professional Advanced ProQuest Library Science ProQuest Central Korea Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) ProQuest Computing ProQuest One Social Sciences ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Business (Alumni) ProQuest Central (Alumni) Business Premium Collection (Alumni) |
| DatabaseTitleList | ProQuest Business Collection (Alumni Edition) |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1573-7462 |
| EndPage | 4135 |
| ExternalDocumentID | A718212766 10_1007_s10462_020_09949_9 |
| GroupedDBID | -4Z -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 1N0 1SB 2.D 203 23N 28- 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6J9 6NX 77K 7WY 8AO 8FE 8FG 8FL 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AAHNG AAIAL AAJKR AAJSJ AAKKN AANZL AAOBN AARHV AARTL AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABEEZ ABFTD ABFTV ABHLI ABHQN ABIVO ABJNI ABJOX ABKCH ABKTR ABMNI ABMOR ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACACY ACBXY ACGFS ACHSB ACHXU ACIHN ACKNC ACMDZ ACMLO ACOKC ACOMO ACREN ACSNA ACULB ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEAQA AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFFNX AFGCZ AFGXO AFKRA AFLOW AFQWF AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALSLI ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ C24 C6C CAG CCPQU CNYFK COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DWQXO EBLON EBS EDO EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IAO IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV KOW LAK LLZTM M0C M0N M1O M4Y MA- MK~ N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P62 P9O PF0 PQBIZ PQBZA PQQKQ PROAC PSYQQ PT5 Q2X QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TEORI TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WH7 WK8 YLTOR Z45 Z5O Z7R Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8R Z8S Z8T Z8U Z8W Z92 ZMTXR ~A9 ~EX 77I AAFWJ AASML AAYXX ABDBE ABFSG ACSTC ADHKG AEZWR AFFHD AFHIU AGQPQ AHPBZ AHWEU AIXLP AYFIA CITATION ICD PHGZM PHGZT PQGLB PRQQA 7SC 7T9 7XB 8AL 8FD 8FK E3H F2A JQ2 L.- L7M L~C L~D PKEHL PQEST PQUKI PRINS Q9U |
| ID | FETCH-LOGICAL-c358t-bb7ec06f01bbe27a73e09c5825a8eea1976df962c477895022e1e6e5087bda5c3 |
| IEDL.DBID | M0C |
| ISICitedReferencesCount | 12 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000618923000002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0269-2821 |
| IngestDate | Sat Nov 15 10:11:40 EST 2025 Sat Nov 29 09:49:08 EST 2025 Sat Nov 29 02:43:25 EST 2025 Tue Nov 18 22:27:37 EST 2025 Fri Feb 21 02:48:10 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Keywords | Grammatical swarm Automatic programming Medical data classification Swarm programming |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c358t-bb7ec06f01bbe27a73e09c5825a8eea1976df962c477895022e1e6e5087bda5c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-8267-0304 |
| PQID | 2554500559 |
| PQPubID | 36790 |
| PageCount | 39 |
| ParticipantIDs | proquest_journals_2554500559 gale_infotracacademiconefile_A718212766 crossref_citationtrail_10_1007_s10462_020_09949_9 crossref_primary_10_1007_s10462_020_09949_9 springer_journals_10_1007_s10462_020_09949_9 |
| PublicationCentury | 2000 |
| PublicationDate | 20210800 2021-08-00 20210801 |
| PublicationDateYYYYMMDD | 2021-08-01 |
| PublicationDate_xml | – month: 8 year: 2021 text: 20210800 |
| PublicationDecade | 2020 |
| PublicationPlace | Dordrecht |
| PublicationPlace_xml | – name: Dordrecht |
| PublicationSubtitle | An International Science and Engineering Journal |
| PublicationTitle | The Artificial intelligence review |
| PublicationTitleAbbrev | Artif Intell Rev |
| PublicationYear | 2021 |
| Publisher | Springer Netherlands Springer Springer Nature B.V |
| Publisher_xml | – name: Springer Netherlands – name: Springer – name: Springer Nature B.V |
| References | DehuriSRoyRChoSBGhoshAAn improved swarm optimized functional link artificial neural network (iso-flann) for classificationJ Syst Softw20128561333134510.1016/j.jss.2012.01.025 WinklerSAffenzellerMWagnerSAdvanced genetic programming based machine learningJ Math Model Algor200763455480232898310.1007/s10852-007-9065-6 YehYCWangWJChiouCWCardiac arrhythmia diagnosis method using linear discriminant analysis on ecg signalsMeasurement200942577878910.1016/j.measurement.2009.01.004 ZhangMWongPGenetic programming for medical classification: a program simplification approachGenet Progr Evolvable Mach20089322925510.1007/s10710-008-9059-9 BrameierMBanzhafWA comparison of linear genetic programming and neural networks in medical data miningIEEE Trans Evolut Comput200151172610.1109/4235.910462 McKayRIHoaiNXWhighamPAShanYO’NeillMGrammar-based genetic programming: a surveyGenet Progr Evolvable Mach20101136539610.1007/s10710-010-9109-y NagKPalNRA multiobjective genetic programming-based ensemble for simultaneous feature selection and classificationIEEE Trans Cybern201546249951010.1109/TCYB.2015.2404806 Paul TK, Iba H (2006) Classification of scleroderma and normal biopsy data and identification of possible biomarkers of the disease. In: 2006 IEEE symposium on computational intelligence and bioinformatics and computational biology. IEEE, pp 1–6 SiTDuttaRPartial opposition-based particle swarm optimizer in artiżcial neural network training for medical data classiżcationInt J Inf Technol Decis Mak20191851717175010.1142/S0219622019500329 Si T, De A, Bhattacharjee AK (2013) Grammatical bee colony. In: Panigrahi BK, Suganthan PN, Das S, Dash SS (eds) Swarm, evolutionary, and memetic computing. SEMCCO, Lecture Notes in computer science, vol 8297. Springer, New York, pp 436–445 Valencia-Ramírez JM, Raya JA, Cedeno JR, Suárez RR, Escalante HJ, Graff M (2014) Comparison between genetic programming and full model selection on classification problems. In: 2014 IEEE international autumn meeting on power, electronics and computing (ROPEC). IEEE, pp 1–6 Assunçao F, Lourenço N, Machado P, Ribeiro B (2017) Automatic generation of neural networks with structured grammatical evolution. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE, pp 1557–1564 Asuncion A, Newman D (2007) Uci machine learning repository. University of California, School of Information and Computer Science, Irvine, CA. http://archive.ics.uci.edu/ml/datasets.php Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. In: Technical Report-TR06, Erciyes University. Engineering Faculty, Computer Engineering Department, Kayseri/Türkiye Yasodha P, Ananthanarayanan N (2018) Detecting the ovarian cancer using big data analysis with effective model Elleuch S, Jarboui B (2018) Variable neighborhood programming for evolving discriminent functions with dynamic thresholds. In: 2018 international conference on machine learning and cybernetics (ICMLC), vol 1. IEEE, pp 263–268 Kou G, LU Y, Peng Y, Shi Y (2012) Evaluation of classification algorithms using mcdm and rank correlation. Int J Inf Technol Decis Mak 11:197–225. https://doi.org/10.1142/S0219622012500095 BarandelaRSánchezJSGarcaVRangelEStrategies for learning in class imbalance problemsPattern Recogn200336384985110.1016/S0031-3203(02)00257-1 Contreras, I., Bertachi, A., Biagi, L., Vehí, J., Oviedo, S.: Using grammatical evolution to generate short-term blood glucose prediction models. In: KHD@ IJCAI, pp 91–96 López-Vázquez G, Ornelas-Rodriguez M, Espinal A, Soria-Alcaraz JA, Rojas-Domínguez A, Puga-Soberanes H, Carpio JM (2019) Rostro-Gonzalez H (2019) Evolutionary spiking neural networks for solving supervised classification problems. Comput Intell Neurosci 2019:4182639. https://doi.org/10.1155/2019/4182639 Motsinger-ReifAADeodharSWinhamSJHardisonNEGrammatical evolution decision trees for detecting gene-gene interactionsBioData Min201031810.1186/1756-0381-3-8 O’NeillMBrabazonAGrammatical swarm: the generation of programs by social programmingNat Comput200654443462228905310.1007/s11047-006-9007-7 LuzEJSSchwartzWRCámara-ChávezGMenottiDEcg-based heartbeat classification for arrhythmia detection: a surveyComput Methods Progr Biomed201612714416410.1016/j.cmpb.2015.12.008 Si T (2016) Grammatical evolution using fireworks algorithm. In: Pant M, Deep K, Bansal J, Nagar A, Das K (eds) Proceedings of 5th international conference on soft computing for problem solving. Advances in intelligent systems and computing, vol 436, pp. 43–55. Springer, New York SiTSkSA comparison of grammatical bee colony and neural networks in medical data miningInt J Comput Appl2016134614 Gray H, Maxwell R, Martinez-Perez I, Arus C, Cerdan S (1996) Genetic programming for classification of brain tumours from nuclear magnetic resonance biopsy spectra. Genet Progr 424:1–6 SiTDeABhattacharjeeAKSegmentation of brain mri using wavelet transform and grammatical bee colonyJ Circuits, Syst Comput20182707185010810.1142/S0218126618501086 Chareka T, Pillay N (2016) A study of fitness functions for data classification using grammatical evolution. In: 2016 Pattern recognition association of South Africa and robotics and mechatronics international conference (PRASA-RobMech). IEEE, pp. 1–4 HanJPeiJKamberMData mining: concepts and techniques2011AmsterdamElsevier1445.68004 Hope D, Munday E, Smith S (2007) Evolutionary algorithms in the classification of mammograms. In: 2007 IEEE symposium on computational intelligence in image and signal processing. IEEE, pp 258–265 ZhaoHA multi-objective genetic programming approach to developing pareto optimal decision treesDecis Support Syst200743380982610.1016/j.dss.2006.12.011 LinJYKeHRChienBCYangWPClassifier design with feature selection and feature extraction using layered genetic programmingExpert Syst Appl20083421384139310.1016/j.eswa.2007.01.006 RiveroDDoradoJRabuñalJPazosAGeneration and simplification of artificial neural networks by means of genetic programmingNeurocomputing20107316–183200322310.1016/j.neucom.2010.05.010 Mckay RI, Hoai NX, Whigham PA, Shan Y, O’neill M (2010a) Grammar-based genetic programming: a survey. Genet Progr Evolvable Mach 11(3–4):365–396 Cerri R, Barros RC, de Carvalho AC, Freitas AA (2013) A grammatical evolution algorithm for generation of hierarchical multi-label classification rules. In: 2013 IEEE congress on evolutionary computation. IEEE, pp 454–461 LennartssonDNordinPA genetic programming method for the identification of signal peptides and prediction of their cleavage sitesEURASIP J Adv Signal Process2004115369710.1155/S1110865704309108 EspejoPGVenturaSHerreraFA survey on the application of genetic programming to classificationIEEE Trans Syst, Man, Cybern, Part C (Appl Rev)200940212114410.1109/TSMCC.2009.2033566 Triantaphyllou E (2000) Multi-criteria decision making methods: a comparative study 44. https://doi.org/10.1007/978-1-4757-3157-6 O’Neill M, Ryan C (2003) Grammatical evolution. In: Grammatical evolution. Springer, US, pp 33–47 NeillMBrabazonAGrammatical swarmGenet Evolut Comput Conf20041163174 Tharwat A (2018) Classification assessment methods. Appl Comput Inf. https://doi.org/10.1016/j.aci.2018.08.003 O’NeillMRyanCGrammatical evolutionIEEE Trans Evolut Comput20015434935810.1109/4235.942529 TanYZhuYFireworks algorithm for optimizationInternational conference in swarm intelligence2010New YorkSpringer355364 S Dehuri (9949_CR8) 2012; 85 J Han (9949_CR12) 2011 9949_CR20 D Rivero (9949_CR29) 2010; 73 9949_CR40 9949_CR27 9949_CR28 H Zhao (9949_CR43) 2007; 43 M Neill (9949_CR24) 2004; 1 RI McKay (9949_CR21) 2010; 11 S Winkler (9949_CR39) 2007; 6 M Brameier (9949_CR4) 2001; 5 D Lennartsson (9949_CR16) 2004; 1 K Nag (9949_CR23) 2015; 46 Y Tan (9949_CR35) 2010 9949_CR9 M O’Neill (9949_CR26) 2001; 5 M O’Neill (9949_CR25) 2006; 5 9949_CR30 9949_CR31 T Si (9949_CR32) 2018; 27 9949_CR13 EJS Luz (9949_CR19) 2016; 127 9949_CR11 9949_CR38 9949_CR14 9949_CR36 M Zhang (9949_CR42) 2008; 9 9949_CR15 9949_CR37 PG Espejo (9949_CR10) 2009; 40 9949_CR18 R Barandela (9949_CR3) 2003; 36 JY Lin (9949_CR17) 2008; 34 T Si (9949_CR33) 2019; 18 9949_CR2 9949_CR1 AA Motsinger-Reif (9949_CR22) 2010; 3 YC Yeh (9949_CR41) 2009; 42 9949_CR6 T Si (9949_CR34) 2016; 134 9949_CR5 9949_CR7 |
| References_xml | – reference: Gray H, Maxwell R, Martinez-Perez I, Arus C, Cerdan S (1996) Genetic programming for classification of brain tumours from nuclear magnetic resonance biopsy spectra. Genet Progr 424:1–6 – reference: HanJPeiJKamberMData mining: concepts and techniques2011AmsterdamElsevier1445.68004 – reference: Paul TK, Iba H (2006) Classification of scleroderma and normal biopsy data and identification of possible biomarkers of the disease. In: 2006 IEEE symposium on computational intelligence and bioinformatics and computational biology. IEEE, pp 1–6 – reference: O’NeillMRyanCGrammatical evolutionIEEE Trans Evolut Comput20015434935810.1109/4235.942529 – reference: Si T (2016) Grammatical evolution using fireworks algorithm. In: Pant M, Deep K, Bansal J, Nagar A, Das K (eds) Proceedings of 5th international conference on soft computing for problem solving. Advances in intelligent systems and computing, vol 436, pp. 43–55. Springer, New York – reference: RiveroDDoradoJRabuñalJPazosAGeneration and simplification of artificial neural networks by means of genetic programmingNeurocomputing20107316–183200322310.1016/j.neucom.2010.05.010 – reference: TanYZhuYFireworks algorithm for optimizationInternational conference in swarm intelligence2010New YorkSpringer355364 – reference: LuzEJSSchwartzWRCámara-ChávezGMenottiDEcg-based heartbeat classification for arrhythmia detection: a surveyComput Methods Progr Biomed201612714416410.1016/j.cmpb.2015.12.008 – reference: Hope D, Munday E, Smith S (2007) Evolutionary algorithms in the classification of mammograms. In: 2007 IEEE symposium on computational intelligence in image and signal processing. IEEE, pp 258–265 – reference: López-Vázquez G, Ornelas-Rodriguez M, Espinal A, Soria-Alcaraz JA, Rojas-Domínguez A, Puga-Soberanes H, Carpio JM (2019) Rostro-Gonzalez H (2019) Evolutionary spiking neural networks for solving supervised classification problems. Comput Intell Neurosci 2019:4182639. https://doi.org/10.1155/2019/4182639 – reference: Chareka T, Pillay N (2016) A study of fitness functions for data classification using grammatical evolution. In: 2016 Pattern recognition association of South Africa and robotics and mechatronics international conference (PRASA-RobMech). IEEE, pp. 1–4 – reference: EspejoPGVenturaSHerreraFA survey on the application of genetic programming to classificationIEEE Trans Syst, Man, Cybern, Part C (Appl Rev)200940212114410.1109/TSMCC.2009.2033566 – reference: NagKPalNRA multiobjective genetic programming-based ensemble for simultaneous feature selection and classificationIEEE Trans Cybern201546249951010.1109/TCYB.2015.2404806 – reference: Valencia-Ramírez JM, Raya JA, Cedeno JR, Suárez RR, Escalante HJ, Graff M (2014) Comparison between genetic programming and full model selection on classification problems. In: 2014 IEEE international autumn meeting on power, electronics and computing (ROPEC). IEEE, pp 1–6 – reference: O’NeillMBrabazonAGrammatical swarm: the generation of programs by social programmingNat Comput200654443462228905310.1007/s11047-006-9007-7 – reference: Yasodha P, Ananthanarayanan N (2018) Detecting the ovarian cancer using big data analysis with effective model – reference: Asuncion A, Newman D (2007) Uci machine learning repository. University of California, School of Information and Computer Science, Irvine, CA. http://archive.ics.uci.edu/ml/datasets.php – reference: Motsinger-ReifAADeodharSWinhamSJHardisonNEGrammatical evolution decision trees for detecting gene-gene interactionsBioData Min201031810.1186/1756-0381-3-8 – reference: Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. In: Technical Report-TR06, Erciyes University. Engineering Faculty, Computer Engineering Department, Kayseri/Türkiye – reference: SiTDeABhattacharjeeAKSegmentation of brain mri using wavelet transform and grammatical bee colonyJ Circuits, Syst Comput20182707185010810.1142/S0218126618501086 – reference: ZhangMWongPGenetic programming for medical classification: a program simplification approachGenet Progr Evolvable Mach20089322925510.1007/s10710-008-9059-9 – reference: LennartssonDNordinPA genetic programming method for the identification of signal peptides and prediction of their cleavage sitesEURASIP J Adv Signal Process2004115369710.1155/S1110865704309108 – reference: BarandelaRSánchezJSGarcaVRangelEStrategies for learning in class imbalance problemsPattern Recogn200336384985110.1016/S0031-3203(02)00257-1 – reference: SiTDuttaRPartial opposition-based particle swarm optimizer in artiżcial neural network training for medical data classiżcationInt J Inf Technol Decis Mak20191851717175010.1142/S0219622019500329 – reference: YehYCWangWJChiouCWCardiac arrhythmia diagnosis method using linear discriminant analysis on ecg signalsMeasurement200942577878910.1016/j.measurement.2009.01.004 – reference: LinJYKeHRChienBCYangWPClassifier design with feature selection and feature extraction using layered genetic programmingExpert Syst Appl20083421384139310.1016/j.eswa.2007.01.006 – reference: BrameierMBanzhafWA comparison of linear genetic programming and neural networks in medical data miningIEEE Trans Evolut Comput200151172610.1109/4235.910462 – reference: WinklerSAffenzellerMWagnerSAdvanced genetic programming based machine learningJ Math Model Algor200763455480232898310.1007/s10852-007-9065-6 – reference: Assunçao F, Lourenço N, Machado P, Ribeiro B (2017) Automatic generation of neural networks with structured grammatical evolution. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE, pp 1557–1564 – reference: DehuriSRoyRChoSBGhoshAAn improved swarm optimized functional link artificial neural network (iso-flann) for classificationJ Syst Softw20128561333134510.1016/j.jss.2012.01.025 – reference: Cerri R, Barros RC, de Carvalho AC, Freitas AA (2013) A grammatical evolution algorithm for generation of hierarchical multi-label classification rules. In: 2013 IEEE congress on evolutionary computation. IEEE, pp 454–461 – reference: McKayRIHoaiNXWhighamPAShanYO’NeillMGrammar-based genetic programming: a surveyGenet Progr Evolvable Mach20101136539610.1007/s10710-010-9109-y – reference: NeillMBrabazonAGrammatical swarmGenet Evolut Comput Conf20041163174 – reference: ZhaoHA multi-objective genetic programming approach to developing pareto optimal decision treesDecis Support Syst200743380982610.1016/j.dss.2006.12.011 – reference: Si T, De A, Bhattacharjee AK (2013) Grammatical bee colony. In: Panigrahi BK, Suganthan PN, Das S, Dash SS (eds) Swarm, evolutionary, and memetic computing. SEMCCO, Lecture Notes in computer science, vol 8297. Springer, New York, pp 436–445 – reference: Triantaphyllou E (2000) Multi-criteria decision making methods: a comparative study 44. https://doi.org/10.1007/978-1-4757-3157-6 – reference: SiTSkSA comparison of grammatical bee colony and neural networks in medical data miningInt J Comput Appl2016134614 – reference: Elleuch S, Jarboui B (2018) Variable neighborhood programming for evolving discriminent functions with dynamic thresholds. In: 2018 international conference on machine learning and cybernetics (ICMLC), vol 1. IEEE, pp 263–268 – reference: Mckay RI, Hoai NX, Whigham PA, Shan Y, O’neill M (2010a) Grammar-based genetic programming: a survey. Genet Progr Evolvable Mach 11(3–4):365–396 – reference: Contreras, I., Bertachi, A., Biagi, L., Vehí, J., Oviedo, S.: Using grammatical evolution to generate short-term blood glucose prediction models. In: KHD@ IJCAI, pp 91–96 – reference: Kou G, LU Y, Peng Y, Shi Y (2012) Evaluation of classification algorithms using mcdm and rank correlation. Int J Inf Technol Decis Mak 11:197–225. https://doi.org/10.1142/S0219622012500095 – reference: O’Neill M, Ryan C (2003) Grammatical evolution. In: Grammatical evolution. Springer, US, pp 33–47 – reference: Tharwat A (2018) Classification assessment methods. Appl Comput Inf. https://doi.org/10.1016/j.aci.2018.08.003 – ident: 9949_CR11 – ident: 9949_CR15 doi: 10.1142/S0219622012500095 – ident: 9949_CR38 doi: 10.1109/ROPEC.2014.7036349 – volume: 18 start-page: 1717 issue: 5 year: 2019 ident: 9949_CR33 publication-title: Int J Inf Technol Decis Mak doi: 10.1142/S0219622019500329 – volume: 6 start-page: 455 issue: 3 year: 2007 ident: 9949_CR39 publication-title: J Math Model Algor doi: 10.1007/s10852-007-9065-6 – ident: 9949_CR40 – ident: 9949_CR37 doi: 10.1007/978-1-4757-3157-6 – ident: 9949_CR20 doi: 10.1007/s10710-010-9109-y – ident: 9949_CR28 doi: 10.1109/CIBCB.2006.330951 – volume: 73 start-page: 3200 issue: 16–18 year: 2010 ident: 9949_CR29 publication-title: Neurocomputing doi: 10.1016/j.neucom.2010.05.010 – start-page: 355 volume-title: International conference in swarm intelligence year: 2010 ident: 9949_CR35 – volume: 127 start-page: 144 year: 2016 ident: 9949_CR19 publication-title: Comput Methods Progr Biomed doi: 10.1016/j.cmpb.2015.12.008 – ident: 9949_CR5 doi: 10.1109/CEC.2013.6557604 – volume: 1 start-page: 153697 year: 2004 ident: 9949_CR16 publication-title: EURASIP J Adv Signal Process doi: 10.1155/S1110865704309108 – ident: 9949_CR18 doi: 10.1155/2019/4182639 – volume: 5 start-page: 349 issue: 4 year: 2001 ident: 9949_CR26 publication-title: IEEE Trans Evolut Comput doi: 10.1109/4235.942529 – ident: 9949_CR36 doi: 10.1016/j.aci.2018.08.003 – volume: 134 start-page: 1 issue: 6 year: 2016 ident: 9949_CR34 publication-title: Int J Comput Appl – ident: 9949_CR13 doi: 10.1109/CIISP.2007.369178 – ident: 9949_CR1 doi: 10.1109/CEC.2017.7969488 – volume: 43 start-page: 809 issue: 3 year: 2007 ident: 9949_CR43 publication-title: Decis Support Syst doi: 10.1016/j.dss.2006.12.011 – volume: 9 start-page: 229 issue: 3 year: 2008 ident: 9949_CR42 publication-title: Genet Progr Evolvable Mach doi: 10.1007/s10710-008-9059-9 – volume: 36 start-page: 849 issue: 3 year: 2003 ident: 9949_CR3 publication-title: Pattern Recogn doi: 10.1016/S0031-3203(02)00257-1 – volume: 3 start-page: 8 issue: 1 year: 2010 ident: 9949_CR22 publication-title: BioData Min doi: 10.1186/1756-0381-3-8 – ident: 9949_CR7 – volume: 40 start-page: 121 issue: 2 year: 2009 ident: 9949_CR10 publication-title: IEEE Trans Syst, Man, Cybern, Part C (Appl Rev) doi: 10.1109/TSMCC.2009.2033566 – ident: 9949_CR14 – volume: 85 start-page: 1333 issue: 6 year: 2012 ident: 9949_CR8 publication-title: J Syst Softw doi: 10.1016/j.jss.2012.01.025 – volume: 1 start-page: 163 year: 2004 ident: 9949_CR24 publication-title: Genet Evolut Comput Conf – volume: 42 start-page: 778 issue: 5 year: 2009 ident: 9949_CR41 publication-title: Measurement doi: 10.1016/j.measurement.2009.01.004 – ident: 9949_CR27 doi: 10.1007/978-1-4615-0447-4_4 – ident: 9949_CR31 doi: 10.1007/978-3-319-03753-0_39 – volume: 11 start-page: 365 year: 2010 ident: 9949_CR21 publication-title: Genet Progr Evolvable Mach doi: 10.1007/s10710-010-9109-y – volume: 5 start-page: 17 issue: 1 year: 2001 ident: 9949_CR4 publication-title: IEEE Trans Evolut Comput doi: 10.1109/4235.910462 – ident: 9949_CR6 doi: 10.1109/RoboMech.2016.7813165 – volume: 46 start-page: 499 issue: 2 year: 2015 ident: 9949_CR23 publication-title: IEEE Trans Cybern doi: 10.1109/TCYB.2015.2404806 – ident: 9949_CR30 doi: 10.1007/978-981-10-0448-3_4 – volume: 27 start-page: 1850108 issue: 07 year: 2018 ident: 9949_CR32 publication-title: J Circuits, Syst Comput doi: 10.1142/S0218126618501086 – ident: 9949_CR9 doi: 10.1109/ICMLC.2018.8526925 – volume: 34 start-page: 1384 issue: 2 year: 2008 ident: 9949_CR17 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2007.01.006 – ident: 9949_CR2 – volume-title: Data mining: concepts and techniques year: 2011 ident: 9949_CR12 – volume: 5 start-page: 443 issue: 4 year: 2006 ident: 9949_CR25 publication-title: Nat Comput doi: 10.1007/s11047-006-9007-7 |
| SSID | ssj0005243 |
| Score | 2.344758 |
| Snippet | In a computational medical model, diagnosis is the classification of disease status in the terms of
abnormal
or
positive
,
normal
or
negative
or
intermediate... In a computational medical model, diagnosis is the classification of disease status in the terms of abnormal or positive, normal or negative or intermediate... |
| SourceID | proquest gale crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 4097 |
| SubjectTerms | Algorithms Artificial Intelligence Artificial neural networks Automatic Classification Classifiers Computer Science Data Decision analysis Decision-making Diagnosis Discriminant analysis Disease Experiments Extraction False positive results Feature extraction Geometric accuracy Grammar Grammar, Comparative and general Machine learning Medical advice systems Medical diagnosis Medical model Multiple criterion Neural networks Performance evaluation |
| SummonAdditionalLinks | – databaseName: Springer Nature Consortium list (Orbis Cascade Alliance) dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LSwMxEA5aPXixPrFaJQfBgwa6u0l2462I1YMU8UXxEpJsFgTbSrv19ztJs7Y-Qc95MpNkZsg33yB0KBTPde6gUjqlhOYmIgocYWJpVBie5NRYX7XkKu12s15PXIeksHGFdq--JP1LPZfsRnlMXLgDXg0VRCyiJTB3mSvYcHP7MAfsmGLlYi4IBBRRSJX5fo4P5ujzo_zld9QbnU79f9tdQ6vBycTt6alYRwt2sIHqVQEHHO7zJnq8GCmXu0acLcuxmpRDz-CKA2qrDwti8Gpxf_qdgx2eFBvncDuEkVfqKVYDPF8nAHvG2i103zm_O7skodgCMQnLSqJ1ak2LF61IaxunKk1sSxgGAaTKrFURuC15IXhsaJpmgoHpt5HlFvy7VOeKmWQb1QbDgd1B2BRJZjRvGRZpqhNop4VljCUQ7hTGZg0UVTKXJjCRu4IYz3LGoeyEJ0F40gtPigY6fh_zMuXh-LX3kVOldJcUZjYq5BrA_hzdlWyDRXbU9pw3ULPStgy3dywhzKLMsZPBRCeVdmfNP6-7-7fue2gldhAZjydsolo5mth9tGxey6fx6MCf6jdMW_Ex priority: 102 providerName: Springer Nature |
| Title | Grammar-based automatic programming for medical data classification: an experimental study |
| URI | https://link.springer.com/article/10.1007/s10462-020-09949-9 https://www.proquest.com/docview/2554500559 |
| Volume | 54 |
| WOSCitedRecordID | wos000618923000002&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: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1573-7462 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005243 issn: 0269-2821 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB7RlgMXWl7qQln5gMQBLOLEj5gLKlULErCsyqv0EtmOIyHR3bK75fcz43W6bRG9cLEUJXGsjB_f2N98A_DEOt36lqhS3kgu2yC4QyDMoxRd0FUrQ0xZS96b0ag-OrLjvOE2z7TKfk5ME3U7DbRH_gKhr1SkGGVfnf7ilDWKTldzCo012CBkQ5S-D8XeBYrHkjVXasvRtRA5aCaHzkldcnKeECNJy-2lhenq9PzXOWlafg42_7fhW3A7A0-2u-wpd-BGnNyFzT6pA8tj_B4cv5k5imfjtL61zJ0tpknVlWUm1wk2jyHSZSfLIx5GHFMWCIQT6ygZ-iVzE3YxdwBLKrb34cvB_ue9tzwnYOChUvWCe29iKHRXCO9jaZypYmGDQqfS1TE6gVCm7awugzSmtgrhQBRRR8R8xrdOheoBrE-mk7gNLHRVHbwughJe-grvyy4qpSp0gboQ6wGI_u83IauTU5KMn81KV5ks1qDFmmSxxg7g2fk7p0ttjmuffkpGbWjgYs3B5fgDbB9JYDW7uEqT3L3WA9jpLdnkET1vVmYcwPO-L6xu__u7D6-v7RHcKokmkziFO7C-mJ3Fx3Az_F78mM-GsGa-fR_Cxuv90fgQr94ZPkx9nErxEcuxOsby8NPXP_3eAXM |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VggQXylMsFPABxAEsNokfMRJCFVBa7bLiUKSKi7EdR0Jqd9vdLYg_xW9kxnHYAqK3HjgncRLn8zzib74BeGScanxDVCmvBRdNKLjDQJhHUbRBVY0IMXUtGevJpN7fNx_W4EdfC0O0yt4mJkPdzAL9I3-Ooa-QpBhlXh0dc-oaRburfQuNDhaj-P0bpmyLl7tv8Ps-Lsvtt3uvd3juKsBDJesl917HMFTtsPA-ltrpKg5NkJgpuTpGV6B_blqjyiC0ro1EHxeLqCIGMto3ToYKx70AF0VVa1pXI81PUUo6ll6pDMdUpshFOrlUT6iSU7KGMZkw3PzmCP90B3_tyyZ3t73xv03UNbiaA2u21a2E67AWpzdgo29awbINuwmf3s0d1etx8t8NcyfLWVKtZZmpdojTwTCSZ4fdFhYjDi0LlGQQqyoB-QVzU3a6NwJLKr234OO5vOFtWJ_OpvEOsNBWdfBqGGThha_wuGijlLLCFK8NsR5A0X9tG7L6OjUBObAr3WhCiEWE2IQQawbw9Nc1R532yJlnPyEQWTJMOHJwub4Cn48kvuwWRiEk56_UADZ75NhssRZ2BZsBPOuxtzr87_vePXu0h3B5Z-_92I53J6N7cKUkSlDiT27C-nJ-Eu_DpfB1-WUxf5DWEoPP543Jn5DQWDg |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VghAXyqtioYAPIA5gdZP4ESMhVFEWqlarPYBU9WJsx5GQ6G7Z3YL4a_w6ZhyHLSB664GzE-f1eR7xN98APDZONb4hqpTXgosmFNxhIMyjKNqgqkaEmLqWHOjxuD48NJM1-NHXwhCtsreJyVA3s0D_yLcx9BWSFKPMdptpEZPd0auTL5w6SNFOa99Oo4PIfvz-DdO3xcu9XfzWT8py9Ob963c8dxjgoZL1knuvYxiqdlh4H0vtdBWHJkjMmlwdoyvQVzetUWUQWtdGor-LRVQRgxrtGydDhfNegssac0yiE07k0Rl6ScfYK5XhmNYUuWAnl-0JVXJK3DA-E4ab35zin67hrz3a5PpGG__zS7sB13PAzXa6FXIT1uL0Fmz0zSxYtm234ejt3FEdHye_3jB3upwlNVuWGWzH-GoYRvjsuNvaYsStZYGSD2JbJYC_YG7KzvZMYEm99w58uJAn3IT16Wwa7wILbVUHr4ZBFl74CsdFG6WUFaZ-bYj1AIr-y9uQVdmpOchnu9KTJrRYRItNaLFmAM9-nXPSaZKce_RTApQlg4UzB5frLvD-SPrL7mB0QjL_Sg1gq0eRzZZsYVcQGsDzHoer4X9f9975sz2CqwhFe7A33r8P10piCiVa5RasL-en8QFcCV-Xnxbzh2lZMfh40ZD8CT0OYVw |
| 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=Grammar-based+automatic+programming+for+medical+data+classification%3A+an+experimental+study&rft.jtitle=The+Artificial+intelligence+review&rft.au=Si%2C+Tapas&rft.au=Miranda%2C+P%C3%A9ricles&rft.au=Galdino%2C+Jo%C3%A3o+Victor&rft.au=Nascimento%2C+Andr%C3%A9&rft.date=2021-08-01&rft.issn=0269-2821&rft.eissn=1573-7462&rft.volume=54&rft.issue=6&rft.spage=4097&rft.epage=4135&rft_id=info:doi/10.1007%2Fs10462-020-09949-9&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s10462_020_09949_9 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0269-2821&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0269-2821&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0269-2821&client=summon |