{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:15:26Z","timestamp":1761808526768,"version":"3.40.3"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031082764"},{"type":"electronic","value":"9783031082771"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-08277-1_14","type":"book-chapter","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T12:13:01Z","timestamp":1655381581000},"page":"165-179","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Feature Selection for\u00a0Credit Risk Classification"],"prefix":"10.1007","author":[{"given":"Dalia","family":"Atif","sequence":"first","affiliation":[]},{"given":"Mabrouka","family":"Salmi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,17]]},"reference":[{"issue":"1","key":"14_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12920-020-00826-6","volume":"13","author":"A Acharjee","year":"2020","unstructured":"Acharjee, A., Larkman, J., Xu, Y., Cardoso, V.R., Gkoutos, G.V.: A random forest based biomarker discovery and power analysis framework for diagnostics research. BMC Med. Genomics 13(1), 1\u201314 (2020)","journal-title":"BMC Med. Genomics"},{"key":"14_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105936","volume":"86","author":"N Arora","year":"2020","unstructured":"Arora, N., Kaur, P.D.: A bolasso based consistent feature selection enabled random forest classification algorithm: an application to credit risk assessment. Appl. Soft Comput. 86, 105936 (2020)","journal-title":"Appl. Soft Comput."},{"issue":"2","key":"14_CR3","first-page":"23","volume":"9","author":"G Arutjothi","year":"2017","unstructured":"Arutjothi, G., Senthamarai, C.: Credit risk evaluation using hybrid feature selection method. Softw. Eng. 9(2), 23\u201326 (2017)","journal-title":"Softw. Eng."},{"key":"14_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.impact.2019.100179","volume":"15","author":"A Bahl","year":"2019","unstructured":"Bahl, A., et al.: Recursive feature elimination in random forest classification supports nanomaterial grouping. NanoImpact 15, 100179 (2019)","journal-title":"NanoImpact"},{"issue":"2","key":"14_CR5","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF00058655","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123\u2013140 (1996). https:\/\/doi.org\/10.1007\/BF00058655","journal-title":"Mach. Learn."},{"issue":"1","key":"14_CR6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/a:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001). https:\/\/doi.org\/10.1023\/a:1010933404324","journal-title":"Mach. Learn."},{"key":"14_CR7","first-page":"1","volume":"2020","author":"W Chen","year":"2020","unstructured":"Chen, W., Li, Z., hui Guo, J.: A vns-eda algorithm-based feature selection for credit risk classification. Math. Prob. Eng. 2020, 1\u201314 (2020)","journal-title":"Math. Prob. Eng."},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Chi, G., Uddin, M.S., Habib, T., Zhou, Y., Islam, M.R., Chowdhury, M.A.I.: A hybrid model for credit risk assessment: empirical validation by real-world credit data. J. Risk Model Validation, 14(4) (2019)","DOI":"10.21314\/JRMV.2020.228"},{"issue":"9","key":"14_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.14569\/IJARAI.2016.050901","volume":"5","author":"S Dahiya","year":"2016","unstructured":"Dahiya, S., Handa, S., Singh, N.: A rank aggregation algorithm for ensemble of multiple feature selection techniques in credit risk evaluation. Int. J. Adv. Res. Artif. Intell. 5(9), 1\u20138 (2016)","journal-title":"Int. J. Adv. Res. Artif. Intell."},{"issue":"1","key":"14_CR10","first-page":"1","volume":"19","author":"BF Darst","year":"2018","unstructured":"Darst, B.F., Malecki, K.C., Engelman, C.D.: Using recursive feature elimination in random forest to account for correlated variables in high dimensional data. BMC Genet. 19(1), 1\u20136 (2018)","journal-title":"BMC Genet."},{"issue":"9","key":"14_CR11","doi-asserted-by":"publisher","first-page":"400","DOI":"10.3390\/agriculture10090400","volume":"10","author":"D Elavarasan","year":"2020","unstructured":"Elavarasan, D., Vincent, P.M.D.R., Srinivasan, K., Chang, C.Y.: A hybrid cfs filter and rf-rfe wrapper-based feature extraction for enhanced agricultural crop yield prediction modeling. Agriculture 10(9), 400 (2020)","journal-title":"Agriculture"},{"issue":"1","key":"14_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v033.i01","volume":"33","author":"J Friedman","year":"2010","unstructured":"Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1), 1 (2010)","journal-title":"J. Stat. Softw."},{"key":"14_CR13","unstructured":"Genuer, R., Poggi, J.M.: Arbres cart et for\u00eats al\u00e9atoires, importance et s\u00e9lection de variables (2017). arXiv preprint arXiv: 1610.08203"},{"issue":"14","key":"14_CR14","doi-asserted-by":"publisher","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","volume":"31","author":"R Genuer","year":"2010","unstructured":"Genuer, R., Poggi, J.M., Tuleau-Malot, C.: Variable selection using random forests. Pattern Recogn. Lett. 31(14), 2225\u20132236 (2010)","journal-title":"Pattern Recogn. Lett."},{"issue":"2","key":"14_CR15","doi-asserted-by":"publisher","first-page":"19","DOI":"10.32614\/RJ-2015-018","volume":"7","author":"R Genuer","year":"2015","unstructured":"Genuer, R., Poggi, J.M., Tuleau-Malot, C.: Vsurf: an r package for variable selection using random forests. R J. 7(2), 19\u201333 (2015)","journal-title":"R J."},{"issue":"3","key":"14_CR16","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1007\/s11222-016-9646-1","volume":"27","author":"B Gregorutti","year":"2017","unstructured":"Gregorutti, B., Michel, B., Saint-Pierre, P.: Correlation and variable importance in random forests. Stat. Comput. 27(3), 659\u2013678 (2017). https:\/\/doi.org\/10.1007\/s11222-016-9646-1","journal-title":"Stat. Comput."},{"key":"14_CR17","series-title":"Springer Series in Statistics","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-19425-7","volume-title":"Regression Modeling Strategies","author":"FE Harrell","year":"2015","unstructured":"Harrell, F.E.: Regression Modeling Strategies. SSS, Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-19425-7"},{"issue":"3","key":"14_CR18","first-page":"129","volume":"7","author":"MAM Hasan","year":"2016","unstructured":"Hasan, M.A.M., Nasser, M., Ahmad, S., Molla, K.I.: Feature selection for intrusion detection using random forest. J. Inf. Secur. 7(3), 129\u2013140 (2016)","journal-title":"J. Inf. Secur."},{"issue":"4","key":"14_CR19","first-page":"579","volume":"35","author":"T Hastie","year":"2020","unstructured":"Hastie, T., Tibshirani, R., Tibshirani, R.: Best subset, forward stepwise or lasso? analysis and recommendations based on extensive comparisons. Stat. Sci. 35(4), 579\u2013592 (2020)","journal-title":"Stat. Sci."},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Huang, Y., Montoya, A.: Lack of robustness of lasso and group lasso with categorical predictors: impact of coding strategy on variable selection and prediction (2020). arXiv preprint arXiv:40b200z6","DOI":"10.31234\/osf.io\/wc45u"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Jovi\u0107, A., Brki\u0107, K., Bogunovi\u0107, N.: A review of feature selection methods with applications. In: 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1200\u20131205. IEEE (2015)","DOI":"10.1109\/MIPRO.2015.7160458"},{"issue":"13","key":"14_CR22","doi-asserted-by":"publisher","first-page":"5125","DOI":"10.1016\/j.eswa.2013.03.019","volume":"40","author":"J Kruppa","year":"2013","unstructured":"Kruppa, J., Schwarz, A., Arminger, G., Ziegler, A.: Consumer credit risk: Individual probability estimates using machine learning. Expert Syst. Appl. 40(13), 5125\u20135131 (2013)","journal-title":"Expert Syst. Appl."},{"issue":"7","key":"14_CR23","doi-asserted-by":"publisher","first-page":"746","DOI":"10.3390\/math9070746","volume":"9","author":"J Laborda","year":"2021","unstructured":"Laborda, J., Ryoo, S.: Feature selection in a credit scoring model. Mathematics 9(7), 746 (2021)","journal-title":"Mathematics"},{"key":"14_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107391","volume":"107","author":"PZ Lappas","year":"2021","unstructured":"Lappas, P.Z., Yannacopoulos, A.N.: A machine learning approach combining expert knowledge with genetic algorithms in feature selection for credit risk assessment. Appl. Soft Comput. 107, 107391 (2021)","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"14_CR25","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.ejor.2015.05.030","volume":"247","author":"S Lessmann","year":"2015","unstructured":"Lessmann, S., Baesens, B., Seow, H.V., Thomas, L.C.: Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research. Eur. J. Oper. Res. 247(1), 124\u2013136 (2015)","journal-title":"Eur. J. Oper. Res."},{"issue":"5","key":"14_CR26","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1109\/TCSS.2021.3074534","volume":"8","author":"G Mariammal","year":"2021","unstructured":"Mariammal, G., Suruliandi, A., Raja, S., Poongothai, E.: Prediction of land suitability for crop cultivation based on soil and environmental characteristics using modified recursive feature elimination technique with various classifiers. IEEE Trans. Comput. Soc. Syst. 8(5), 1132\u20131142 (2021)","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"issue":"9","key":"14_CR27","doi-asserted-by":"publisher","first-page":"2652","DOI":"10.3390\/nu12092652","volume":"12","author":"AJ McEligot","year":"2020","unstructured":"McEligot, A.J., Poynor, V., Sharma, R., Panangadan, A.: Logistic lasso regression for dietary intakes and breast cancer. Nutrients 12(9), 2652 (2020)","journal-title":"Nutrients"},{"key":"14_CR28","unstructured":"Molina, L.C., Belanche, L., Nebot, \u00c0.: Feature selection algorithms: a survey and experimental evaluation. In: 2002 IEEE International Conference on Data Mining, 2002. Proceedings, pp. 306\u2013313. IEEE (2002)"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Mustaqeem, A., Anwar, S.M., Majid, M., Khan, A.R.: Wrapper method for feature selection to classify cardiac arrhythmia. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3656\u20133659. IEEE (2017)","DOI":"10.1109\/EMBC.2017.8037650"},{"issue":"4","key":"14_CR30","doi-asserted-by":"publisher","first-page":"518","DOI":"10.15837\/ijccc.2019.4.3563","volume":"14","author":"W Nazih","year":"2019","unstructured":"Nazih, W., Hifny, Y., Elkilani, W., Abdelkader, T., Faheem, H.: Efficient detection of attacks in sip based voip networks using linear l1-svm classifier. Int. J. Comput. Commun. Control 14(4), 518\u2013529 (2019)","journal-title":"Int. J. Comput. Commun. Control"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Pandey, T.N., Jagadev, A.K., Mohapatra, S.K., Dehuri, S.: Credit risk analysis using machine learning classifiers. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), pp. 1850\u20131854. IEEE (2017)","DOI":"10.1109\/ICECDS.2017.8389769"},{"issue":"4","key":"14_CR32","doi-asserted-by":"publisher","first-page":"1715","DOI":"10.1109\/TPWRD.2019.2918316","volume":"34","author":"X Peng","year":"2019","unstructured":"Peng, X., et al.: Random forest based optimal feature selection for partial discharge pattern recognition in hv cables. IEEE Trans. Power Deliv. 34(4), 1715\u20131724 (2019)","journal-title":"IEEE Trans. Power Deliv."},{"key":"14_CR33","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.artmed.2017.11.003","volume":"84","author":"MS Rahman","year":"2018","unstructured":"Rahman, M.S., Rahman, M.K., Kaykobad, M., Rahman, M.S.: isGPT: an optimized model to identify sub-golgi protein types using svm and random forest based feature selection. Artif. Intell. Med. 84, 90\u2013100 (2018)","journal-title":"Artif. Intell. Med."},{"key":"14_CR34","doi-asserted-by":"crossref","unstructured":"Ramya, R., Kumaresan, S.: Analysis of feature selection techniques in credit risk assessment. In: 2015 International Conference on Advanced Computing and Communication Systems, pp. 1\u20136. IEEE (2015)","DOI":"10.1109\/ICACCS.2015.7324139"},{"key":"14_CR35","doi-asserted-by":"publisher","unstructured":"Salmi, M., Atif, D.: Using a data mining approach to detect automobile insurance fraud. In: International Conference on Soft Computing and Pattern Recognition, pp. 55\u201366. Springer (2021). https:\/\/doi.org\/10.1007\/978-3-030-96302-6_5","DOI":"10.1007\/978-3-030-96302-6_5"},{"key":"14_CR36","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.neucom.2018.10.085","volume":"342","author":"B Seijo-Pardo","year":"2019","unstructured":"Seijo-Pardo, B., et al.: Biases in feature selection with missing data. Neurocomputing 342, 97\u2013112 (2019)","journal-title":"Neurocomputing"},{"issue":"1","key":"14_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-018-0143-6","volume":"5","author":"G Smith","year":"2018","unstructured":"Smith, G.: Step away from stepwise. J. Big Data 5(1), 1\u201312 (2018). https:\/\/doi.org\/10.1186\/s40537-018-0143-6","journal-title":"J. Big Data"},{"key":"14_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1007\/978-3-540-25966-4_33","volume-title":"Multiple Classifier Systems","author":"V Svetnik","year":"2004","unstructured":"Svetnik, V., Liaw, A., Tong, C., Wang, T.: Application of breiman\u2019s random forest to modeling structure-activity relationships of pharmaceutical molecules. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 334\u2013343. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-25966-4_33"},{"issue":"1","key":"14_CR39","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc.: B (Methodol) 58(1), 267\u2013288 (1996)","journal-title":"J. Roy. Stat. Soc.: B (Methodol)"},{"issue":"2","key":"14_CR40","doi-asserted-by":"publisher","first-page":"e0117844","DOI":"10.1371\/journal.pone.0117844","volume":"10","author":"H Wang","year":"2015","unstructured":"Wang, H., Xu, Q., Zhou, L.: Large unbalanced credit scoring using lasso-logistic regression ensemble. PLOS ONE 10(2), e0117844 (2015)","journal-title":"PLOS ONE"},{"key":"14_CR41","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Uddin, M.S., Habib, T., Chi, G., Yuan, K.: Feature selection in credit risk modeling: an international evidence. Economic Research-Ekonomska Istra\u017eivanja, pp. 1\u201331 (2020)","DOI":"10.1080\/1331677X.2020.1867213"}],"container-title":["Communications in Computer and Information Science","Intelligent Systems and Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-08277-1_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T01:41:03Z","timestamp":1727401263000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-08277-1_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031082764","9783031082771"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-08277-1_14","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"17 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Systems and Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hammamet","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tunisia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 March 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 March 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ispr22022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ispr2022.sciencesconf.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"91","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was held online.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}