2017 5th International Conference on Cyber and IT Service Management (CITSM) 2017
DOI: 10.1109/citsm.2017.8089264
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Combining integreted sampling technique with feature selection for software defect prediction

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Cited by 10 publications
(9 citation statements)
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“…The proposed approach led to a more accurate identification of faulty software modules. In [49], the researchers combined an integrated sampling method with FS that effectively addresses the problems of class imbalance and attribute relevance in SDP. Three attribute selection algorithms were used: chi-square (CS), information gain (IG), and relief (RLF).…”
Section: A Rq1: Which Feature Selection Methods Are Implemented For S...mentioning
confidence: 99%
“…The proposed approach led to a more accurate identification of faulty software modules. In [49], the researchers combined an integrated sampling method with FS that effectively addresses the problems of class imbalance and attribute relevance in SDP. Three attribute selection algorithms were used: chi-square (CS), information gain (IG), and relief (RLF).…”
Section: A Rq1: Which Feature Selection Methods Are Implemented For S...mentioning
confidence: 99%
“…The additional data amounted to approximately 40% for the feature task and approximately 23% for the conjunction task. We chose the oversampling method for addressing the problem of rare events as a number of studies showed that oversampling is suitable for working with Naïve Bayesian classifiers (Maloof, 2003; Mohammed et al, 2020; Putri & Frieyadie, 2017). Our approach was to initially train the models on oversampled data, and following this training, test them on the actual data.…”
Section: Methodsmentioning
confidence: 99%
“…Then research of (Putri and Frieyadie, 2017) proposes a method of sampling techniques are integrated with the method of selection of features. The method of sample selection used SMOTE.…”
Section: Literature Reviewmentioning
confidence: 99%