{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:22:36Z","timestamp":1743027756277,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319957852"},{"type":"electronic","value":"9783319957869"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","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":[[2018]]},"DOI":"10.1007\/978-3-319-95786-9_17","type":"book-chapter","created":{"date-parts":[[2018,7,3]],"date-time":"2018-07-03T17:55:52Z","timestamp":1530640552000},"page":"230-239","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Decision Rule Based Approach to Generational Feature Selection"],"prefix":"10.1007","author":[{"given":"Wies\u0142aw","family":"Paja","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,7,4]]},"reference":[{"key":"17_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-6849-3","volume-title":"Applied Predictive Modeling","author":"M Kuhn","year":"2013","unstructured":"Kuhn, M., Johnson, K.: Applied Predictive Modeling. Springer, New York (2013). https:\/\/doi.org\/10.1007\/978-1-4614-6849-3"},{"key":"17_CR2","series-title":"The Springer International Series in Engineering and Computer Science","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/978-1-4615-5689-3_3","volume-title":"Feature Selection for Knowledge Discovery and Data Mining","author":"H Liu","year":"1998","unstructured":"Liu, H., et al.: Feature selection aspects. In: Liu, H., Motoda, H. (eds.) Feature Selection for Knowledge Discovery and Data Mining. The Springer International Series in Engineering and Computer Science, vol. 454, pp. 43\u201372. Springer, Boston (1998). https:\/\/doi.org\/10.1007\/978-1-4615-5689-3_3"},{"key":"17_CR3","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1186\/1471-2105-8-150","volume":"8","author":"R Nilsson","year":"2007","unstructured":"Nilsson, R., Pe\u00f1a, J.M., Bj\u00f6rkegren, J., Tegn\u00e9r, J.: Detecting multivariate differentially expressed genes. BMC Bioinform. 8, 150 (2007)","journal-title":"BMC Bioinform."},{"key":"17_CR4","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.5194\/gmd-9-1065-2016","volume":"9","author":"W Paja","year":"2016","unstructured":"Paja, W., Wrzesie\u0144, M., Niemiec, R., Rudnicki, W.R.: Application of all-relevant feature selection for the failure analysis of parameter-induced simulation crashes in climate models. Geosci. Model Dev. 9, 1065\u20131072 (2016)","journal-title":"Geosci. Model Dev."},{"key":"17_CR5","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/978-3-662-45620-0_2","volume-title":"Feature Selection for Data and Pattern Recognition","author":"WR Rudnicki","year":"2015","unstructured":"Rudnicki, W.R., Wrzesie\u0144, M., Paja, W.: All relevant feature selection methods and applications. In: Sta\u0144czyk, U., Jain, L.C. (eds.) Feature Selection for Data and Pattern Recognition. SCI, vol. 584, pp. 11\u201328. Springer, Heidelberg (2015). https:\/\/doi.org\/10.1007\/978-3-662-45620-0_2"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Mach. Learn. 46, 389\u2013422 (2002)","DOI":"10.1023\/A:1012487302797"},{"issue":"17","key":"17_CR7","doi-asserted-by":"publisher","first-page":"2136","DOI":"10.1093\/bioinformatics\/btq345","volume":"26","author":"M Johannes","year":"2010","unstructured":"Johannes, M., Brase, J.C., Frohlich, H., Gade, S., Gehrmann, M., Falth, M., Sultmann, H., Beiflbarth, T.: Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients. Bioinformatics 26(17), 2136\u20132144 (2010)","journal-title":"Bioinformatics"},{"key":"17_CR8","first-page":"1399","volume":"3","author":"H Stoppiglia","year":"2003","unstructured":"Stoppiglia, H., Dreyfus, G., Dubois, R., Oussar, Y.: Ranking a random feature for variable and feature selection. J. Mach. Learn. Res. 3, 1399\u20131414 (2003)","journal-title":"J. Mach. Learn. Res."},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Tuv, E., Borisov, A., Torkkola, K.: Feature selection using ensemble based ranking against artificial contrasts. In: International Symposium on Neural Networks, pp. 2181\u20132186 (2006)","DOI":"10.1109\/IJCNN.2006.246991"},{"key":"17_CR10","series-title":"Smart Innovation, Systems and Technologies","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-319-39627-9_6","volume-title":"Intelligent Decision Technologies 2016","author":"W Paja","year":"2016","unstructured":"Paja, W.: Feature selection methods based on decision rule and tree models. In: Czarnowski, I., Caballero, A.M., Howlett, R.J., Jain, L.C. (eds.) Intelligent Decision Technologies 2016. SIST, vol. 57, pp. 63\u201370. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-39627-9_6"},{"key":"17_CR11","series-title":"Smart Innovation, Systems and Technologies","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1007\/978-3-319-59421-7_13","volume-title":"Intelligent Decision Technologies 2017","author":"W Paja","year":"2018","unstructured":"Paja, W.: Generational feature elimination to find all relevant feature subset. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds.) IDT 2017. SIST, vol. 72, pp. 140\u2013148. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-59421-7_13"},{"key":"17_CR12","unstructured":"Guyon, I., Gunn, S., Ben-Hur, A., Dror, G.: Result analysis of the NIPS 2003: feature selection challenge. In: Advances in Neural Information Processing Systems, vol. 17, pp. 545\u2013552 (2013)"},{"key":"17_CR13","series-title":"Intelligent Systems Reference Library","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/978-3-319-67588-6_6","volume-title":"Advances in Feature Selection for Data and Pattern Recognition","author":"W Paja","year":"2018","unstructured":"Paja, W., Pancerz, K., Grochowalski, P.: Generational feature elimination and some other ranking feature selection methods. In: Sta\u0144czyk, U., Zielosko, B., Jain, L.C. (eds.) Advances in Feature Selection for Data and Pattern Recognition. ISRL, vol. 138, pp. 97\u2013112. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-67588-6_6"}],"container-title":["Lecture Notes in Computer Science","Advances in Data Mining. Applications and Theoretical Aspects"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-95786-9_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:24:56Z","timestamp":1710260696000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-95786-9_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319957852","9783319957869"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-95786-9_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"4 July 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Industrial Conference on Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New York, NY","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 July 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 July 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"incdm2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.data-mining-forum.de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"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":"146","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":"24","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":"15","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":"16% - 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":"2","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":"0","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)"}}]}}