{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:27:24Z","timestamp":1777703244246,"version":"3.51.4"},"reference-count":43,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2019,6,11]],"date-time":"2019-06-11T00:00:00Z","timestamp":1560211200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2019,6,11]]},"abstract":"<jats:p>\u00a0In order to minimize the over-fitting and related factors that are caused by the high dimensionality of the input data in software defect prediction, the attributes are often optimized using various feature selection techniques. However, the comparative performance of these selection techniques in combination with machine learning algorithms remains largely unexplored using web applications. In this work, we investigate the best possible combination of feature selection technique with machine learning algorithms, with the sample space chosen from open source Apache Click and Rave data sets. Our results are based on 945 defect prediction models derived from parametric, non-parametric and ensemble-based machine learning algorithms, for which the metrics are derived from the various filter and threshold-based ranking techniques. Friedman and Nemenyi post-hoc statistical tests are adopted to identify the performance difference of these models. We find that filter-based feature selection in combination with ensemble-based machine learning algorithms not only poise as the best strategy but also yields a maximum feature set redundancy by 94%, with little or no comprise on the performance index.<\/jats:p>","DOI":"10.3233\/jifs-18473","type":"journal-article","created":{"date-parts":[[2019,6,14]],"date-time":"2019-06-14T15:44:08Z","timestamp":1560527048000},"page":"6567-6578","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Empirical assessment of feature selection techniques in defect prediction models using web applications"],"prefix":"10.1177","volume":"36","author":[{"given":"Ruchika","family":"Malhotra","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Delhi Technological University, Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anjali","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Delhi Technological University, Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,6,11]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-011-9173-9"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2007.256941"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2008.36"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2008.35"},{"key":"e_1_3_1_6_2","first-page":"181","article-title":"Practical Feature Subset Selection For Machine Learning","author":"Hall M.A.","year":"1998","unstructured":"HallM.A. and SmithL.A., Practical Feature Subset Selection For Machine Learning, Proc of the 21st Australasian Computer Science Springer Conference, 1998, pp. 181\u2013191.","journal-title":"Proc of the 21st Australasian Computer Science Springer Conference"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-55860-247-2.50037-1"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2005.112"},{"key":"e_1_3_1_9_2","volume-title":"Ph D Diss Dept of Computer Science","author":"Hall M.A.","year":"1998","unstructured":"HallM.A., Correlation-based Feature Selection for Machine Learning, Ph D Diss Dept of Computer Science, Waikato Univ, 1998."},{"key":"e_1_3_1_10_2","first-page":"69","article-title":"How many software metrics should be selected for defect prediction?","author":"Wang H.","year":"2011","unstructured":"WangH., KhoshgoftaarT.M. and SeliyaN., How many software metrics should be selected for defect prediction? 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