A Novel Resampling Technique for Imbalanced Classification in Software Defect Prediction by a re-sampling method with filtering

Abstract

One of the greatest difficulties that most algorithms that learn classifiers face is imbalanced data. A class imbalance problem, however, is not inherently detrimental, some contemporary studies claim, nor is the performance decline solely attributable to this issue, but rather other aspects of the data distribution, such as the presence of noise and borderline cases around the class heads. In order to rectify the issue of data imbalance, the author proposes a new hybrid preprocessing technique in this study. This technique handles class imbalance, the presence of noise, and borderline samples in software defect data. To solve this problem of data imbalance and noisy samples, a combination of hyperparameter optimization based on Rough Set Theory (RST), Iterative-Partitioning Filter (IPF), and Synthetic Minority Oversampling Technique (SMOTE) oversampling is used. The strategy begins with the application of SMOTE to synthesize artificial examples through a process of linear interpolation. The first step of the algorithm is to use SMOTE followed by linear interpolation of two defect-prone k-nearest neighbors to generate synthetic examples. Then, majority-class examples with a lower approximation of these originals and newly generated minority-class examples are removed using RST. Then IPF is applied to erase the data. We analyze the efficacy of our algorithm by conducting experiments where the learning algorithm is the C4.5 classifier. Then, statistical tests show the superiority of our proposed method over state-of-the-art sampling methods.