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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknik Informatika (JUTIF) International Journal of Electronics and Communications Systems Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Saputro, Setyo Wahyu
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology

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Comparative Study of Filter, Wrapper, and Hybrid Feature Selection Using Tree-Based Classifiers for Software Defect Prediction Rahmayanti, Rahmayanti; Herteno, Rudy; Saputro, Setyo Wahyu; Saragih, Triando Hamonangan; Abadi, Friska
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.294

Abstract

Software defect prediction (SDP) is essential for improving software reliability by enabling the early identification of modules that may contain defects before the release stage. SDP commonly exhibits redundant or non-contributory metrics, underscoring the need for feature selection to derive a more informative subset. To address this problem, the present study investigates and compares the effectiveness of three feature-selection strategies: SelectKBest (SKB), Recursive Feature Elimination (RFE), and the hybrid SKB+RFE, in enhancing the performance of tree-based classifiers on the NASA Metrics Data Program (MDP) data collections. The study utilizes three classification algorithms, namely Random Forest (RF), Extra Trees (ET), and Bagging (Decision Tree), with Area Under the Curve (AUC) serving as the primary metric for assessing model performance. Experimental results reveal that the RFE and Extra Trees combination yields the top performance, producing an average AUC of 0.7855. This is subsequently followed by the SKB+RFE+ET configuration, which achieves an AUC of 0.7809, and SKB+ET at 0.7776. These findings demonstrate that iterative wrapper-based approaches such as RFE can identify more relevant and effective feature subsets than filter or hybrid strategies, with the RFE+Extra Trees configuration yielding the strongest overall predictive performance and wrapper-based methods exhibiting higher stability across heterogeneous datasets. Even without hyperparameter tuning and relying solely on class-weighting rather than explicit resampling techniques, the findings offer empirical insight into the isolated influence of feature selection on predictive performance. Overall, the study confirms that RFE combined with Extra Trees offers the strongest predictive performance on NASA MDP data collections and forms a foundation for developing more adaptive and robust models.
Co-Authors Adini, Muhammad Hifdzi Aflaha, Rahmina Ulfah Andi Farmadi Angga Maulana Akbar Anshory, Muhammad Naufal Badali, Rahmat Amin Deni Sutaji Dodon Turianto Nugrahadi Dwi Kartini, Dwi Erick Kurniawan Fatma Indriani Febrian, Muhamad Michael Friska Abadi Herteno, Rudy Hertono, Rudy Irwan Budiman Kamil, Hawariul Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Muliadi Radityo Adi Nugroho Rahmayanti Rahmayanti Rozaq, Hasri Akbar Awal Rudy Herteno Saragih, Triando Hamonangan Siti Aisyah Solechah Suryadi, Mulia Kevin Syahputra, Muhammad Reza Umiatin, Umiatin Wardana, Muhammad Difha YILDIZ, Oktay