{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T17:29:11Z","timestamp":1767979751782,"version":"3.49.0"},"reference-count":81,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T00:00:00Z","timestamp":1632873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Feature selection is known to be an applicable solution to address the problem of high dimensionality in software defect prediction (SDP). However, choosing an appropriate filter feature selection (FFS) method that will generate and guarantee optimal features in SDP is an open research issue, known as the filter rank selection problem. As a solution, the combination of multiple filter methods can alleviate the filter rank selection problem. In this study, a novel adaptive rank aggregation-based ensemble multi-filter feature selection (AREMFFS) method is proposed to resolve high dimensionality and filter rank selection problems in SDP. Specifically, the proposed AREMFFS method is based on assessing and combining the strengths of individual FFS methods by aggregating multiple rank lists in the generation and subsequent selection of top-ranked features to be used in the SDP process. The efficacy of the proposed AREMFFS method is evaluated with decision tree (DT) and na\u00efve Bayes (NB) models on defect datasets from different repositories with diverse defect granularities. Findings from the experimental results indicated the superiority of AREMFFS over other baseline FFS methods that were evaluated, existing rank aggregation based multi-filter FS methods, and variants of AREMFFS as developed in this study. That is, the proposed AREMFFS method not only had a superior effect on prediction performances of SDP models but also outperformed baseline FS methods and existing rank aggregation based multi-filter FS methods. Therefore, this study recommends the combination of multiple FFS methods to utilize the strength of respective FFS methods and take advantage of filter\u2013filter relationships in selecting optimal features for SDP processes.<\/jats:p>","DOI":"10.3390\/e23101274","type":"journal-article","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T08:27:44Z","timestamp":1632904064000},"page":"1274","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["An Adaptive Rank Aggregation-Based Ensemble Multi-Filter Feature Selection Method in Software Defect Prediction"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7411-3639","authenticated-orcid":false,"given":"Abdullateef O.","family":"Balogun","sequence":"first","affiliation":[{"name":"Department of Computer and Information Science, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Malaysia"},{"name":"Department of Computer Science, University of Ilorin, Ilorin 1515, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuib","family":"Basri","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6966-2369","authenticated-orcid":false,"given":"Luiz Fernando","family":"Capretz","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9300-4363","authenticated-orcid":false,"given":"Saipunidzam","family":"Mahamad","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdullahi A.","family":"Imam","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3890-9792","authenticated-orcid":false,"given":"Malek A.","family":"Almomani","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, The World Islamic Sciences and Education University, Amman 11947, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8398-3609","authenticated-orcid":false,"given":"Victor E.","family":"Adeyemo","sequence":"additional","affiliation":[{"name":"School of Built Environment, Engineering and Computing, Leeds Beckett University, Headingley Campus, Leeds LS6 3QS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0098-0948","authenticated-orcid":false,"given":"Ganesh","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"134","DOI":"10.46792\/fuoyejet.v3i1.178","article-title":"Comparative analysis of selected heterogeneous classifiers for software defects prediction using filter-based feature selection methods","volume":"3","author":"Akintola","year":"2018","journal-title":"FUOYE J. 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