{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T14:48:10Z","timestamp":1778683690639,"version":"3.51.4"},"reference-count":40,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2019,10,17]],"date-time":"2019-10-17T00:00:00Z","timestamp":1571270400000},"content-version":"tdm","delay-in-days":0,"URL":"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":[[2020,1,9]]},"abstract":"<jats:p>The object of Software Defect Prediction (SDP) is to identify modules that are prone to defect. This is achieved by training prediction models with datasets obtained by mining software historical depositories. When one acquires data through this approach, it often includes class imbalance which has an unequal class representation among their example. We hypothesize that the imbalance learning is not a problem in itself and decrease in performance is also influenced by other factors related to class distribution in the data. One of these is the existence of noisy and borderline examples. Thus, the objective of our research is to propose a novel preprocessing method using Synthetic Minority Over-Sampling Technique (SMOTE), Fuzzy-rough Instance Selection type II (FRIS-II) and Iterative Noise Filter based on the Fusion of Classifiers (INFFC) which can overcome these problems. The experimental results show that the new proposal significantly outperformed all the methods compared in this study.<\/jats:p>","DOI":"10.3233\/jifs-179459","type":"journal-article","created":{"date-parts":[[2019,10,18]],"date-time":"2019-10-18T10:57:37Z","timestamp":1571396257000},"page":"917-933","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":11,"title":["SMOTEFRIS-INFFC: Handling the challenge of borderline and noisy examples in imbalanced learning for software defect prediction"],"prefix":"10.1177","volume":"38","author":[{"given":"Kamal","family":"Bashir","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"},{"name":"Department of Information Technology, College of Computer Science and Information Technology, Karary University, Omdurman, Sudan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianrui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chubato Wondaferaw","family":"Yohannese","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahama","family":"Yahaya","sequence":"additional","affiliation":[{"name":"School of Transport and Logistics Engineering, Southwest Jiaotong University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,10,17]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1","article-title":"IEEE standard glossary of software engineering terminology","author":"Radatz J.","year":"1990","unstructured":"RadatzJ., GeraciA. and KatkiF., IEEE standard glossary of software engineering terminology, IEEE Std 610121990(121990) (1990), 1\u201384.","journal-title":"IEEE Std"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2008.35"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.10.027"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"e_1_3_2_6_2","first-page":"1","article-title":"Enhancing software defect prediction using supervised-learning based framework","author":"Bashir K.","year":"2017","unstructured":"BashirK., LiT., YohanneseC. W. and MahamaY., Enhancing software defect prediction using supervised-learning based framework. In 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) (2017), 1\u20136.","journal-title":"12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/2907070"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.2991\/ijcis.2017.10.1.43"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2011.2161285"},{"key":"e_1_3_2_10_2","first-page":"137","article-title":"Attribute selection and imbalanced data: Problems in software defect prediction","volume":"1","author":"Khoshgoftaar T. M.","year":"2010","unstructured":"KhoshgoftaarT. M., GaoK. and SeliyaN., Attribute selection and imbalanced data: Problems in software defect prediction. In 22nd IEEE International Conference on Tools with Artificial Intelligence 1 (2010), 137\u2013144.","journal-title":"22nd IEEE International Conference on Tools with Artificial Intelligence"},{"key":"e_1_3_2_11_2","first-page":"115","article-title":"Editing training sets from imbalanced data using fuzzy-rough sets","author":"Ogawa K.","year":"2015","unstructured":"OgawaK., MatsumotoK. and HashimotoM., Editing training sets from imbalanced data using fuzzy-rough sets. In IFIP International Conference on Artificial Intelligence Applications and Innovations (2015), 115\u2013129.","journal-title":"IFIP International Conference on Artificial Intelligence Applications and Innovations"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2009.2029559"},{"key":"e_1_3_2_13_2","first-page":"1","article-title":"Ensembles based combined learning for improved software fault prediction: A comparative study","author":"Chubato W. Y.","year":"2017","unstructured":"ChubatoW. Y., LiT., SimfukweM. and KhurshidF., Ensembles based combined learning for improved software fault prediction: A comparative study. In International Conference on Intelligent Systems and Knowledge Engineering (ISKE) (2017), 1\u20136.","journal-title":"International Conference on Intelligent Systems and Knowledge Engineering (ISKE)"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2013.2259203"},{"issue":"14","key":"e_1_3_2_15_2","first-page":"26","article-title":"A Three-Stage Based Ensemble Learning for Improved Software Fault Prediction: An Empirical Comparative Study","volume":"10","author":"Chubato W. Y.","year":"2018","unstructured":"ChubatoW. Y., LiT. and BashirK., A Three-Stage Based Ensemble Learning for Improved Software Fault Prediction: An Empirical Comparative Study, International Journal of Computational Intelligence Systems 10(14) (2018), 26.","journal-title":"International Journal of Computational Intelligence Systems"},{"issue":"9","key":"e_1_3_2_16_2","first-page":"1263","article-title":"Learning from imbalanced data","volume":"21","author":"He H.","year":"2008","unstructured":"HeH. and GarciaE. A., Learning from imbalanced data, IEEE Transactions on Knowledge & Data Engineering 21(9) (2008), 1263\u20131284.","journal-title":"IEEE Transactions on Knowledge & Data Engineering"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.09.038"},{"key":"e_1_3_2_18_2","first-page":"63","article-title":"Class imbalances: are we focusing on the right issue. Inpage","volume":"1723","author":"Japkowicz N.","year":"2003","unstructured":"JapkowiczN., Class imbalances: are we focusing on the right issue. Inpage , Workshop on Learning from Imbalanced Data Sets II 1723 (2003), 63.","journal-title":"Workshop on Learning from Imbalanced Data Sets II"},{"key":"e_1_3_2_19_2","first-page":"397","article-title":"An empirical study of the behavior of classifiers on imbalanced and overlapped data sets","author":"Garc\u00eda V.","year":"2007","unstructured":"Garc\u00edaV., S\u00e1nchezJ. and MollinedaR., An empirical study of the behavior of classifiers on imbalanced and overlapped data sets. In Iberoamerican Congress on Pattern Recognition (2007), 397\u2013406.","journal-title":"Iberoamerican Congress on Pattern Recognition"},{"key":"e_1_3_2_20_2","first-page":"158","article-title":"Learning from imbalanced data in presence of noisy and borderline examples","author":"Napiera\u0142a K.","year":"2010","unstructured":"Napiera\u0142aK., StefanowskiJ. and WilkS., Learning from imbalanced data in presence of noisy and borderline examples. In International Conference on Rough Sets and Current Trends in Computing (2010), 158\u2013167.","journal-title":"International Conference on Rough Sets and Current Trends in Computing"},{"key":"e_1_3_2_21_2","first-page":"179","article-title":"Addressing the curse of imbalanced training sets: one-sided selection","volume":"97","author":"Kubat M.","year":"1997","unstructured":"KubatM. and MatwinS., et al., Addressing the curse of imbalanced training sets: one-sided selection. In Icml 97 (1997), 179\u2013186. Nashville, USA.","journal-title":"Icml"},{"key":"e_1_3_2_22_2","doi-asserted-by":"crossref","unstructured":"Bashirk. LiT. ChubatoW. Y. YahayaM. and AliT. A novel preprocessing approach for imbalanced learning in software defect prediction. In 13 International Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018) Belfast Northern Ireland UK. World Scientific (2018).","DOI":"10.1142\/9789813273238_0065"},{"key":"e_1_3_2_23_2","first-page":"1","article-title":"Fuzzy-rough instance selection","author":"Jensen R.","year":"2010","unstructured":"JensenR. and CornelisC., Fuzzy-rough instance selection. In IEEE International Conference on Fuzzy Systems (FUZZ) (2010), 1\u20137.","journal-title":"IEEE International Conference on Fuzzy Systems (FUZZ)"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2014.08.051"},{"key":"e_1_3_2_25_2","first-page":"475","article-title":"Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem","author":"Bunkhumpornpat C.","year":"2009","unstructured":"BunkhumpornpatC., SinapiromsaranK. and LursinsapC., Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (2009), 475\u2013482.","journal-title":"Pacific-Asia Conference on Knowledge Discovery and Data Mining"},{"key":"e_1_3_2_26_2","first-page":"878","article-title":"Borderline-smote: a new over-sampling method in imbalanced data sets learning","author":"Han H.","year":"2005","unstructured":"HanH., WangW.-Y. and MaoB.-H., Borderline-smote: a new over-sampling method in imbalanced data sets learning. In International Conference on Intelligent Computing (2005), 878\u2013887.","journal-title":"International Conference on Intelligent Computing"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.232"},{"key":"e_1_3_2_28_2","first-page":"1322","article-title":"Adasyn: Adaptive synthetic sampling approach for imbalanced learning","author":"He H.","year":"2008","unstructured":"HeH., BaiY., GarciaE. A. and LiS., Adasyn: Adaptive synthetic sampling approach for imbalanced learning. In IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) (2008), 1322\u20131328.","journal-title":"IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/ITAB.2008.4570642"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007735"},{"key":"e_1_3_2_31_2","doi-asserted-by":"crossref","unstructured":"RamentolE. VerbiestN. BelloR. CaballeroY. CornelisC. and HerreraF. Smote-Frst: A New Resampling Method Using Fuzzy Rough Set Theory World Scientific Proceedings Series on Computer Engineering and Information Science (2012) 800\u2013805.","DOI":"10.1142\/9789814417747_0128"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF01001956"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2015.04.002"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/1656274.1656278"},{"issue":"3","key":"e_1_3_2_35_2","first-page":"307","article-title":"KEEL: a software tool to assess evolutionary algorithms for data mining problems","volume":"13","author":"Alcal\u00e1-Fdez J.","year":"2009","unstructured":"Alcal\u00e1-FdezJ., SanchezL., GarciaS., del JesusM. J., VenturaS., GarrellJ. M., OteroJ., RomeroC., BacarditJ. and RivasV. M., KEEL: a software tool to assess evolutionary algorithms for data mining problems, Soft Computing-A Fusion of Foundations, Methodologies and Applications 13(3) (2009), 307\u2013318.","journal-title":"Soft Computing-A Fusion of Foundations, Methodologies and Applications"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-011-9173-9"},{"key":"e_1_3_2_37_2","unstructured":"KrishnaR. PryorD. and MenziesT. The promise repository of empirical software engineering data. http:\/\/openscience.us\/repo\/. North Carolina State University Department of Computer Science (2015)."},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007734"},{"key":"e_1_3_2_39_2","unstructured":"RijsbergenV C Keith Joost Information retrieval Information Retrieval Group University of Glasgow butterworths london 30(6) (1979)."},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.70740"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2009.12.010"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-179459","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-179459","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-179459","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:40:08Z","timestamp":1777455608000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-179459"}},"subtitle":[],"editor":[{"given":"Cengiz","family":"Kahraman","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2019,10,17]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,1,9]]}},"alternative-id":["10.3233\/JIFS-179459"],"URL":"https:\/\/doi.org\/10.3233\/jifs-179459","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,17]]}}}