{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:45:22Z","timestamp":1742924722588,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030378370"},{"type":"electronic","value":"9783030378387"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-37838-7_13","type":"book-chapter","created":{"date-parts":[[2020,1,2]],"date-time":"2020-01-02T20:02:45Z","timestamp":1577995365000},"page":"135-154","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Wrapper-Based Feature Selection Using Self-adaptive Differential Evolution"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9604-0554","authenticated-orcid":false,"given":"Du\u0161an","family":"Fister","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9964-6957","authenticated-orcid":false,"given":"Iztok","family":"Fister","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timotej","family":"Jagri\u010d","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6418-1272","authenticated-orcid":false,"suffix":"Jr.","given":"Iztok","family":"Fister","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5864-3533","authenticated-orcid":false,"given":"Janez","family":"Brest","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,1,3]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","DOI":"10.1201\/b17320","volume-title":"Data Classification: Algorithms and Applications","author":"CC Aggarwal","year":"2014","unstructured":"Aggarwal, C.C.: Data Classification: Algorithms and Applications. CRC Press, Boca Raton (2014)"},{"key":"13_CR2","doi-asserted-by":"publisher","first-page":"39496","DOI":"10.1109\/ACCESS.2019.2906757","volume":"7","author":"Q Al-Tashi","year":"2019","unstructured":"Al-Tashi, Q., Kadir, S.J.A., Rais, H.M., Mirjalili, S., Alhussian, H.: Binary optimization using hybrid grey wolf optimization for feature selection. IEEE Access 7, 39496\u201339508 (2019)","journal-title":"IEEE Access"},{"key":"13_CR3","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1016\/j.asoc.2015.10.037","volume":"38","author":"J Apolloni","year":"2016","unstructured":"Apolloni, J., Leguizam\u00f3n, G., Alba, E.: Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments. Appl. Soft Comput. 38, 922\u2013932 (2016)","journal-title":"Appl. Soft Comput."},{"issue":"7","key":"13_CR4","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1377\/hlthaff.2014.0041","volume":"33","author":"DW Bates","year":"2014","unstructured":"Bates, D.W., Saria, S., Ohno-Machado, L., Shah, A., Escobar, G.: Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff. 33(7), 1123\u20131131 (2014)","journal-title":"Health Aff."},{"key":"13_CR5","doi-asserted-by":"publisher","DOI":"10.1515\/9781400874668","volume-title":"Adaptive Control Processes: A Guided Tour Princeton University Press","author":"R Bellman","year":"1961","unstructured":"Bellman, R.: Adaptive Control Processes: A Guided Tour Princeton University Press. Princeton, New Jersey (1961)"},{"issue":"6","key":"13_CR6","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1109\/TEVC.2006.872133","volume":"10","author":"J Brest","year":"2006","unstructured":"Brest, J., Greiner, S., Bo\u0161kovi\u010d, B., Mernik, M., \u017dumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646\u2013657 (2006)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"2","key":"13_CR7","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/s12293-012-0080-4","volume":"4","author":"L Cardona","year":"2012","unstructured":"Cardona, L., Moreno, L.A.: Cash management cost reduction using data mining to forecast cash demand and LP to optimize resources. Memetic Comput. 4(2), 127\u2013134 (2012)","journal-title":"Memetic Comput."},{"issue":"1","key":"13_CR8","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","volume":"40","author":"G Chandrashekar","year":"2014","unstructured":"Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16\u201328 (2014)","journal-title":"Comput. Electr. Eng."},{"key":"13_CR9","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/keras.io"},{"issue":"5","key":"13_CR10","doi-asserted-by":"publisher","first-page":"1439","DOI":"10.1080\/00207540802473989","volume":"48","author":"C Cunha Da","year":"2010","unstructured":"Da Cunha, C., Agard, B., Kusiak, A.: Selection of modules for mass customisation. Int. J. Prod. Res. 48(5), 1439\u20131454 (2010)","journal-title":"Int. J. Prod. Res."},{"key":"13_CR11","unstructured":"Elsalamony, H.A., Elsayad, A.M.: Bank direct marketing based on neural network. Int. J. Eng. Adv. Technol. (IJEAT) 2(6) (2013)"},{"key":"13_CR12","volume-title":"Introduction to Artificial Intelligence","author":"W Ertel","year":"2018","unstructured":"Ertel, W.: Introduction to Artificial Intelligence. Springer, Heidelberg (2018)"},{"issue":"3","key":"13_CR13","first-page":"37","volume":"17","author":"U Fayyad","year":"1996","unstructured":"Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37\u201337 (1996)","journal-title":"AI Mag."},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Fister, D., Fister, I., Jagri\u010d, T., Fister Jr, I., Brest, J.: A novel self-adaptive differential evolution for feature selection using threshold mechanism. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 17\u201324. IEEE (2018)","DOI":"10.1109\/SSCI.2018.8628715"},{"key":"13_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10247-4","volume-title":"Data Preprocessing in Data Mining","author":"S Garc\u00eda","year":"2015","unstructured":"Garc\u00eda, S., Luengo, J., Herrera, F.: Data Preprocessing in Data Mining. Springer, New York (2015). https:\/\/doi.org\/10.1007\/978-3-319-10247-4"},{"key":"13_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"1\u20132","key":"13_CR17","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","volume":"97","author":"R Kohavi","year":"1997","unstructured":"Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1\u20132), 273\u2013324 (1997)","journal-title":"Artif. Intell."},{"key":"13_CR18","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.engappai.2017.12.014","volume":"70","author":"M Labani","year":"2018","unstructured":"Labani, M., Moradi, P., Ahmadizar, F., Jalili, M.: A novel multivariate filter method for feature selection in text classification problems. Eng. Appl. Artif. Intell. 70, 25\u201337 (2018)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"4","key":"13_CR19","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/s12599-014-0334-4","volume":"6","author":"H Lasi","year":"2014","unstructured":"Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., Hoffmann, M.: Industry 4.0. Bus. Inf. Syst. Eng. 6(4), 239\u2013242 (2014)","journal-title":"Bus. Inf. Syst. Eng."},{"issue":"7553","key":"13_CR20","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)","journal-title":"Nature"},{"issue":"3","key":"13_CR21","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1109\/JAS.2019.1911447","volume":"6","author":"H Liu","year":"2019","unstructured":"Liu, H., Zhou, M.C., Liu, Q.: An embedded feature selection method for imbalanced data classification. IEEE\/CAA J. Automatica Sinica 6(3), 703\u2013715 (2019)","journal-title":"IEEE\/CAA J. Automatica Sinica"},{"key":"13_CR22","doi-asserted-by":"publisher","first-page":"20950","DOI":"10.1109\/ACCESS.2018.2821441","volume":"6","author":"Z-Z Liu","year":"2018","unstructured":"Liu, Z.-Z., Huang, J.-W., Wang, Y., Cao, D.-S.: ECoFFeS: a software using evolutionary computation for feature selection in drug discovery. IEEE Access 6, 20950\u201320963 (2018)","journal-title":"IEEE Access"},{"key":"13_CR23","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.eswa.2018.11.006","volume":"119","author":"L Meng","year":"2019","unstructured":"Meng, L.: Embedded feature selection accounting for unknown data heterogeneity. Expert Syst. Appl. 119, 350\u2013361 (2019)","journal-title":"Expert Syst. Appl."},{"key":"13_CR24","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.asoc.2017.11.006","volume":"62","author":"M Mafarja","year":"2018","unstructured":"Mafarja, M., Mirjalili, S.: Whale optimization approaches for wrapper feature selection. Appl. Soft Comput. 62, 441\u2013453 (2018)","journal-title":"Appl. Soft Comput."},{"key":"13_CR25","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/978-981-13-5934-7_28","volume-title":"Ambient Communications and Computer Systems","author":"P Mallik","year":"2019","unstructured":"Mallik, P., Roy, C., Maheshwari, E., Pandey, M., Rautray, S.: Analyzing student performance using data mining. In: Hu, Y.-C., Tiwari, S., Mishra, K.K., Trivedi, M.C. (eds.) Ambient Communications and Computer Systems. AISC, vol. 904, pp. 307\u2013318. Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-13-5934-7_28"},{"key":"13_CR26","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.dss.2014.03.001","volume":"62","author":"S Moro","year":"2014","unstructured":"Moro, S., Cortez, P., Rita, P.: A data-driven approach to predict the success of bank telemarketing. Decis. Support Syst. 62, 22\u201331 (2014)","journal-title":"Decis. Support Syst."},{"key":"13_CR27","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1186\/s13638-016-0623-3","volume":"1","author":"O Osanaiye","year":"2016","unstructured":"Osanaiye, O., Cai, H., Choo, K.-K.R., Dehghantanha, A., Xu, Z., Dlodlo, M.: Ensemble-based multi-filter feature selection method for DDOS detection in cloud computing. EURASIP J. Wirel. Commun. Netw. 1, 130 (2016)","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"issue":"2","key":"13_CR28","first-page":"692","volume":"7","author":"T Parlar","year":"2017","unstructured":"Parlar, T., Acaravci, S.K.: Using data mining techniques for detecting the important features of the bank direct marketing data. Int. J. Econ. Fin. Issues 7(2), 692\u2013696 (2017)","journal-title":"Int. J. Econ. Fin. Issues"},{"key":"13_CR29","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"7","key":"13_CR30","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.3390\/rs10071117","volume":"10","author":"R Pullanagari","year":"2018","unstructured":"Pullanagari, R., Kereszturi, G., Yule, I.: Integrating airborne hyperspectral, topographic, and soil data for estimating pasture quality using recursive feature elimination with random forest regression. Rem. Sens. 10(7), 1117 (2018)","journal-title":"Rem. Sens."},{"key":"13_CR31","unstructured":"Ramjee, S., Gamal, A.E.: Efficient wrapper feature selection using autoencoder and model based elimination. arXiv preprint arXiv:1905.11592 (2019)"},{"key":"13_CR32","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1007\/978-3-319-39384-1_50","volume-title":"Artificial Intelligence and Soft Computing","author":"M Scherer","year":"2016","unstructured":"Scherer, M., Smolag, J., Gaweda, A.: Predicting success of bank direct marketing by neuro-fuzzy systems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS (LNAI), vol. 9693, pp. 570\u2013576. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-39384-1_50"},{"key":"13_CR33","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.dss.2018.01.005","volume":"107","author":"YO Serrano-Silva","year":"2018","unstructured":"Serrano-Silva, Y.O., Villuendas-Rey, Y., Y\u00e1\u00f1ez-M\u00e1rquez, C.: Automatic feature weighting for improving financial decision support systems. Decis. Support Syst. 107, 78\u201387 (2018)","journal-title":"Decis. Support Syst."},{"key":"13_CR34","volume-title":"Evolutionary Optimization Algorithms","author":"D Simon","year":"2013","unstructured":"Simon, D.: Evolutionary Optimization Algorithms. Wiley, Hoboken (2013)"},{"issue":"6","key":"13_CR35","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MITP.2013.55","volume":"15","author":"U Srinivasan","year":"2013","unstructured":"Srinivasan, U., Arunasalam, B.: Leveraging big data analytics to reduce healthcare costs. IT Prof. 15(6), 21\u201328 (2013)","journal-title":"IT Prof."},{"issue":"4","key":"13_CR36","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341\u2013359 (1997)","journal-title":"J. Glob. Optim."},{"key":"13_CR37","doi-asserted-by":"publisher","first-page":"100462","DOI":"10.1016\/j.swevo.2018.10.013","volume":"50","author":"A Viktorin","year":"2018","unstructured":"Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T., Zamuda, A.: Distance based parameter adaptation for success-history based differential evolution. Swarm Evol. Comput. 50, 100462 (2018)","journal-title":"Swarm Evol. Comput."},{"key":"13_CR38","doi-asserted-by":"crossref","unstructured":"Wang, S., Tang, J., Liu, H.: Embedded unsupervised feature selection. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)","DOI":"10.1609\/aaai.v29i1.9211"},{"issue":"12","key":"13_CR39","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1002\/cem.2746","volume":"29","author":"Y Wang","year":"2015","unstructured":"Wang, Y., Huang, J.-J., Zhou, N., Cao, D.-S., Dong, J., Li, H.-X.: Incorporating PLS model information into particle swarm optimization for descriptor selection in QSAR\/QSPR. J. Chemom. 29(12), 627\u2013636 (2015)","journal-title":"J. Chemom."},{"issue":"1","key":"13_CR40","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1109\/TKDE.2013.109","volume":"26","author":"W Xindong","year":"2014","unstructured":"Xindong, W., Zhu, X., Gong-Qing, W., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97\u2013107 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"4","key":"13_CR41","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1109\/TEVC.2015.2504420","volume":"20","author":"B Xue","year":"2016","unstructured":"Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606\u2013626 (2016)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"13_CR42","unstructured":"Yu, L., Liu, H.: Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp. 856\u2013863 (2003)"}],"container-title":["Communications in Computer and Information Science","Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-37838-7_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,9]],"date-time":"2022-10-09T19:27:16Z","timestamp":1665343636000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-37838-7_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030378370","9783030378387"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-37838-7_13","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"3 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SEMCCO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Swarm, Evolutionary, and Memetic Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Maribor","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovenia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 July 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 July 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"semcco2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/semcco2019.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"31","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":"15","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":"0","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":"48% - 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":"1.6","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)"}}]}}