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A feature dependent Naive Bayes approach and its application to the software defect prediction problem

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dc.contributor.advisor Arar, OF
dc.date.accessioned 2020-01-13T07:57:01Z
dc.date.available 2020-01-13T07:57:01Z
dc.date.issued 2017
dc.identifier.citation Arar, OF; Ayan, K; (2017). A feature dependent Naive Bayes approach and its application to the software defect prediction problem. APPLIED SOFT COMPUTING, 59, 209-197
dc.identifier.issn 1568-4946
dc.identifier.uri https://hdl.handle.net/20.500.12619/2471
dc.identifier.uri https://doi.org/10.1016/j.asoc.2017.05.043
dc.description.abstract Naive Bayes is one of the most widely used algorithms in classification problems because of its simplicity, effectiveness, and robustness. It is suitable for many learning scenarios, such as image classification, fraud detection, web mining, and text classification. Naive Bayes is a probabilistic approach based on assumptions that features are independent of each other and that their weights are equally important. However, in practice, features may be interrelated. In that case, such assumptions may cause a dramatic decrease in performance. In this study, by following preprocessing steps, a Feature Dependent Naive Bayes (FDNB) classification method is proposed. Features are included for calculation as pairs to create dependence between one another. This method was applied to the software defect prediction problem and experiments were carried out using widely recognized NASA PROMISE data sets. The obtained results show that this new method is more successful than the standard Naive Bayes approach and that it has a competitive performance with other feature-weighting techniques. A further aim of this study is to demonstrate that to be reliable, a learning model must be constructed by using only training data, as otherwise misleading results arise from the use of the entire data set. (C) 2017 Elsevier B.V. All rights reserved.
dc.language English
dc.publisher ELSEVIER SCIENCE BV
dc.subject Computer Science
dc.title A feature dependent Naive Bayes approach and its application to the software defect prediction problem
dc.type Article
dc.identifier.volume 59
dc.identifier.startpage 197
dc.identifier.endpage 209
dc.contributor.department Sakarya Üniversitesi/Bilgisayar Ve Bilişim Bilimleri Fakültesi/Bilgisayar Mühendisliği Bölümü
dc.contributor.saüauthor Ayan, Kürşat
dc.relation.journal APPLIED SOFT COMPUTING
dc.identifier.wos WOS:000407732600015
dc.identifier.doi 10.1016/j.asoc.2017.05.043
dc.identifier.eissn 1872-9681
dc.contributor.author Omer Faruk Arar
dc.contributor.author Ayan, Kürşat


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