Performance Evaluation of Embedded Feature Selection Techniques over Filter and Wrapper-based Feature Selection Techniques
Shamsuddeen M. Abubakar, Zahraddeen Sufyanu and Ahmed Baita Garko
Keywords: Embedded Feature Selection Techniques, Software Metrics, Defect Prediction
This paper compared the performance of filter-based and wrapper-based feature selection techniques with that of embedded-based feature selection technique. Recursive Feature Elimination (RFE) with SVM Feature selection techniques were explored on Random Forest (F) and Logistic Regression (RFE-LR). Recursive Feature Elimination (RFE) with SVM Feature selection techniques were explored on Random Forest (RF) and Logistic Regression (LR) using ten (10) publicly available defect datasets. SVM-RFE-LR approach of Embedded Feature selection Techniques produced consistent software metrics within the range of 18% to 55% across the datasets, and SVMRFE-RF reported within 33% to 96%. Significance performance was witnessed over Filter and Wrapper based techniques.