查看文章

High dimensionality is a data quality problem that negatively influences the predictive capabilities of prediction models in software defect prediction (SDP). As a viable solution, feature selection (FS) has been used to address the high dimensionality problem in SDP. From existing studies, Filter-based feature selection (FFS) and Wrapper Feature Selection (WFS) are the two basic types of FS methods. WFS methods have been regarded to have superior performance between the two. However, WFS methods have been known to have high computational cost as the number of executions required for feature subset search, evaluation and selection is not known prior. This often leads to overfitting of prediction models due to easy trapping in local maxima. Applying appropriate search method in WFS subset evaluator phase can resolve its trapping in local maxima. Best First Search (BFS) and Greedy Step-wise Search …