KCO: Balancing class distribution in just-in-time software defect prediction using kernel crossover oversampling.

ABSTRACT: The performance of the defect prediction model by using balanced and imbalanced datasets makes a big impact on the discovery of future defects. Current resampling techniques only address the imbalanced datasets without taking into consideration redundancy and noise inherent to the imbalanced datasets. To address the imbalance issue, we propose Kernel Crossover Oversampling (KCO), an oversampling technique based on kernel analysis and crossover interpolation. Specifically, the proposed technique aims to generate balanced datasets by increasing data diversity in order to reduce redundancy and noise. KCO first represents multidimensional features into two-dimensional features by employing Kernel Principal Component Analysis (KPCA). KCO then divides the plotted data distribution by deploying s

SUBMITTER: Ismail AM 

PROVIDER: S-EPMC11008885 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature