{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T23:03:04Z","timestamp":1751324584832},"reference-count":20,"publisher":"World Scientific Pub Co Pte Lt","issue":"05","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Info. Tech. Dec. Mak."],"published-print":{"date-parts":[[2014,9]]},"abstract":"<jats:p> Bank customer churn prediction is one of the key businesses for modern commercial banks. Recently, various methods have been investigated to identify the customers who would leave away. This paper proposed a new framework based on feature clustering and classification technique to help commercial banks make an effective decision on customer churn problem. The proposed method benefits from the result of data explorations, clusters the customer features, and makes a decision with a state-of-the-art classifier. When facing the data with large amount of missing items, it does not directly remove the features by some subjective threshold, but clusters the features through the consideration of the relationship and the missing ratio. Real-world data from a major commercial bank of China verifies the feasibility of our framework in industrial applications. <\/jats:p>","DOI":"10.1142\/s0219622014500680","type":"journal-article","created":{"date-parts":[[2014,7,17]],"date-time":"2014-07-17T22:56:15Z","timestamp":1405637775000},"page":"1013-1027","source":"Crossref","is-referenced-by-count":9,"title":["Customer Churn Prediction Based on Feature Clustering and Nonparallel Support Vector Machine"],"prefix":"10.1142","volume":"13","author":[{"given":"Xi","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Research Center of Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Shi","sequence":"additional","affiliation":[{"name":"Research Center of Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100080, China"},{"name":"College of Information Sciences and Technology, University of Nebraska at Omaha, Omaha, NE 68118, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jongwon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Digital Technology Management, School of Business Administration, Hoseo University, 12, Hoseodae-gil, Dongnam-gu, Cheonan-si, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heung Kee","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Global Entrepreneurship, School of Business Administration, Hoseo University, 12, Hoseodae-gil, Dongnam-gu, Cheonan-si, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heeseok","family":"Lee","sequence":"additional","affiliation":[{"name":"College of Business, Korea Advanced Institute Science and Technology, Dongdaemum-gu, Seoul 130-722, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2014,9,4]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.05.027"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2006.09.038"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1016\/S1874-8651(09)60003-X"},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.06.121"},{"key":"rf9","doi-asserted-by":"publisher","DOI":"10.1126\/science.1136800"},{"key":"rf10","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2013.2279167"},{"key":"rf11","doi-asserted-by":"publisher","DOI":"10.1126\/science.1205438"},{"key":"rf13","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-85729-504-0"},{"key":"rf14","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3264-1"},{"key":"rf15","doi-asserted-by":"publisher","DOI":"10.1142\/4000"},{"key":"rf16","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1068"},{"key":"rf17","volume-title":"Classification and Regression Trees","author":"Breiman L.","year":"1993"},{"key":"rf19","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"rf20","doi-asserted-by":"publisher","DOI":"10.2307\/2347628"},{"key":"rf21","first-page":"37","volume":"6","author":"Aha D.","year":"1991","journal-title":"Machine Learning"},{"key":"rf22","first-page":"157","volume":"46","author":"CPLEX I. I.","year":"2009","journal-title":"International Business Machines Corporation"},{"key":"rf23","doi-asserted-by":"publisher","DOI":"10.1145\/1656274.1656278"},{"key":"rf24","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"rf25","doi-asserted-by":"publisher","DOI":"10.1162\/089976601750264965"},{"key":"rf26","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"}],"container-title":["International Journal of Information Technology &amp; Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0219622014500680","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T17:22:03Z","timestamp":1565112123000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0219622014500680"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,9]]},"references-count":20,"journal-issue":{"issue":"05","published-online":{"date-parts":[[2014,9,4]]},"published-print":{"date-parts":[[2014,9]]}},"alternative-id":["10.1142\/S0219622014500680"],"URL":"https:\/\/doi.org\/10.1142\/s0219622014500680","relation":{},"ISSN":["0219-6220","1793-6845"],"issn-type":[{"value":"0219-6220","type":"print"},{"value":"1793-6845","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,9]]}}}