{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T15:28:00Z","timestamp":1726068480763},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030412982"},{"type":"electronic","value":"9783030412999"}],"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\/978-3-030-41299-9_44","type":"book-chapter","created":{"date-parts":[[2020,2,22]],"date-time":"2020-02-22T09:02:51Z","timestamp":1582362171000},"page":"566-579","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Genetic Programming-Based Simultaneous Feature Selection and Imputation for Symbolic Regression with Incomplete Data"],"prefix":"10.1007","author":[{"given":"Baligh","family":"Al-Helali","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengjie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,2,23]]},"reference":[{"key":"44_CR1","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/978-3-030-03991-2_33","volume-title":"AI 2018: Advances in Artificial Intelligence","author":"B Al-Helali","year":"2018","unstructured":"Al-Helali, B., Chen, Q., Xue, B., Zhang, M.: A hybrid GP-KNN imputation for symbolic regression with missing values. In: Mitrovic, T., Xue, B., Li, X. (eds.) AI 2018. LNCS (LNAI), vol. 11320, pp. 345\u2013357. Springer, Cham (2018). \nhttps:\/\/doi.org\/10.1007\/978-3-030-03991-2_33"},{"key":"44_CR2","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1016\/j.asoc.2019.03.014","volume":"78","author":"S Arslan","year":"2019","unstructured":"Arslan, S., Ozturk, C.: Multi hive artificial bee colony programming for high dimensional symbolic regression with feature selection. Appl. Soft Comput. 78, 515\u2013527 (2019)","journal-title":"Appl. Soft Comput."},{"key":"44_CR3","unstructured":"Austel, V., et al.: Globally optimal symbolic regression. arXiv preprint \narXiv:1710.10720\n\n (2017)"},{"key":"44_CR4","doi-asserted-by":"crossref","unstructured":"Bhardwaj, H., Sakalle, A., Bhardwaj, A., Tiwari, A., Verma, M.: Breast cancer diagnosis using simultaneous feature selection and classification: a genetic programming approach. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 2186\u20132192. IEEE (2018)","DOI":"10.1109\/SSCI.2018.8628935"},{"key":"44_CR5","first-page":"1","volume":"15","author":"SV Buuren","year":"2010","unstructured":"Buuren, S.V., Groothuis-Oudshoorn, K.: MICE: multivariate imputation by chained equations in R. J. Stat. softw. 15, 1\u201368 (2010)","journal-title":"J. Stat. softw."},{"issue":"5","key":"44_CR6","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1109\/TEVC.2017.2683489","volume":"21","author":"Q Chen","year":"2017","unstructured":"Chen, Q., Zhang, M., Xue, B.: Feature selection to improve generalization of genetic programming for high-dimensional symbolic regression. IEEE Trans. Evol. Comput. 21(5), 792\u2013806 (2017)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1\u20132","key":"44_CR7","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/S0020-0255(02)00371-7","volume":"150","author":"JW Davidson","year":"2003","unstructured":"Davidson, J.W., Savic, D.A., Walters, G.A.: Symbolic and numerical regression: experiments and applications. Inf. Sci. 150(1\u20132), 95\u2013117 (2003)","journal-title":"Inf. Sci."},{"issue":"10","key":"44_CR8","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1016\/j.jclinepi.2006.01.014","volume":"59","author":"ART Donders","year":"2006","unstructured":"Donders, A.R.T., Van Der Heijden, G.J., Stijnen, T., Moons, K.G.: A gentle introduction to imputation of missing values. J. Clin. Epidemiol. 59(10), 1087\u20131091 (2006)","journal-title":"J. Clin. Epidemiol."},{"issue":"Jul","key":"44_CR9","first-page":"2171","volume":"13","author":"FA Fortin","year":"2012","unstructured":"Fortin, F.A., Rainville, F.M.D., Gardner, M.A., Parizeau, M., Gagn\u00e9, C.: Deap: evolutionary algorithms made easy. J. Mach. Learn. Res. 13(Jul), 2171\u20132175 (2012)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"44_CR10","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/s00521-009-0295-6","volume":"19","author":"PJ Garc\u00eda-Laencina","year":"2010","unstructured":"Garc\u00eda-Laencina, P.J., Sancho-G\u00f3mez, J.L., Figueiras-Vidal, A.R.: Pattern classification with missing data: a review. Neural Comput. Appl. 19(2), 263\u2013282 (2010)","journal-title":"Neural Comput. Appl."},{"key":"44_CR11","volume-title":"Genetic Programming II, Automatic Discovery of Reusable Subprograms.","author":"JR Koza","year":"1992","unstructured":"Koza, J.R.: Genetic Programming II, Automatic Discovery of Reusable Subprograms. MIT Press, Cambridge (1992)"},{"key":"44_CR12","unstructured":"Loh, P.L., Wainwright, M.J.: High-dimensional regression with noisy and missing data: provable guarantees with non-convexity. In: Advances in Neural Information Processing Systems, pp. 2726\u20132734 (2011)"},{"key":"44_CR13","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/978-3-319-91341-4_7","volume-title":"Evolutionary and Swarm Intelligence Algorithms","author":"K Nag","year":"2019","unstructured":"Nag, K., Pal, N.R.: Genetic programming for classification and feature selection. In: Bansal, J.C., Singh, P.K., Pal, N.R. (eds.) Evolutionary and Swarm Intelligence Algorithms. SCI, vol. 779, pp. 119\u2013141. Springer, Cham (2019). \nhttps:\/\/doi.org\/10.1007\/978-3-319-91341-4_7"},{"key":"44_CR14","volume-title":"C4.5: Programs for Machine Learning","author":"JR Quinlan","year":"2014","unstructured":"Quinlan, J.R.: C4.5: Programs for Machine Learning. Elsevier, San Francisco (2014)"},{"key":"44_CR15","doi-asserted-by":"crossref","unstructured":"Tran, C.T., Zhang, M., Andreae, P.: Multiple imputation for missing data using genetic programming. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 583\u2013590. ACM (2015)","DOI":"10.1145\/2739480.2754665"},{"key":"44_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/978-3-319-30668-1_10","volume-title":"Genetic Programming","author":"CT Tran","year":"2016","unstructured":"Tran, C.T., Zhang, M., Andreae, P.: A genetic programming-based imputation method for classification with missing data. In: Heywood, M.I., McDermott, J., Castelli, M., Costa, E., Sim, K. (eds.) EuroGP 2016. LNCS, vol. 9594, pp. 149\u2013163. Springer, Cham (2016). \nhttps:\/\/doi.org\/10.1007\/978-3-319-30668-1_10"},{"key":"44_CR17","doi-asserted-by":"crossref","unstructured":"Tran, C.T., Zhang, M., Andreae, P., Xue, B.: Multiple imputation and genetic programming for classification with incomplete data. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 521\u2013528. ACM (2017)","DOI":"10.1145\/3071178.3071181"},{"issue":"2","key":"44_CR18","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/2641190.2641198","volume":"15","author":"J Vanschoren","year":"2014","unstructured":"Vanschoren, J., Van Rijn, J.N., Bischl, B., Torgo, L.: Openml: networked science in machine learning. ACM SIGKDD Explor. Newsl. 15(2), 49\u201360 (2014)","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"44_CR19","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1016\/j.neucom.2017.08.050","volume":"273","author":"F Viegas","year":"2018","unstructured":"Viegas, F., et al.: A genetic programming approach for feature selection in highly dimensional skewed data. Neurocomputing 273, 554\u2013569 (2018)","journal-title":"Neurocomputing"},{"key":"44_CR20","doi-asserted-by":"crossref","unstructured":"Xue, B., Zhang, M.: Evolutionary computation for feature manipulation: key challenges and future directions. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 3061\u20133067. IEEE (2016)","DOI":"10.1109\/CEC.2016.7744176"},{"issue":"1","key":"44_CR21","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1145\/3089251.3089252","volume":"10","author":"B Xue","year":"2017","unstructured":"Xue, B., Zhang, M.: Evolutionary feature manipulation in data mining\/big data. ACM SIGEVOlution 10(1), 4\u201311 (2017)","journal-title":"ACM SIGEVOlution"},{"key":"44_CR22","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1007\/3-540-46695-9_16","volume-title":"Advanced Topics in Artificial Intelligence","author":"M Zhang","year":"1999","unstructured":"Zhang, M., Ciesielski, V.: Genetic programming for multiple class object detection. In: Foo, N. (ed.) AI 1999. LNCS (LNAI), vol. 1747, pp. 180\u2013192. Springer, Heidelberg (1999). \nhttps:\/\/doi.org\/10.1007\/3-540-46695-9_16"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-41299-9_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,2,22]],"date-time":"2020-02-22T09:12:34Z","timestamp":1582362754000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-41299-9_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030412982","9783030412999"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-41299-9_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"23 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","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":"26 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acpr2019a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.acpr2019.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":"214","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":"125","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":"58% - 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":"2","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":"for ACPR 2019 Workshops volume accepted 17 full papers and 6 short papers","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)"}}]}}