Limitless Feature Selection: Revolutionizing Evaluation with MH-FSF
DOI:
https://doi.org/10.5753/jbcs.2026.5646Keywords:
Feature Selection, Android Malware Detection, Benchmarking, Reproducibility, Evaluation, Data Preprocessing, Machine Learning, Artificial InteligenceAbstract
Feature selection plays a crucial role in developing effective predictive models by reducing dimensionality and emphasizing the most relevant attributes. However, current research in this area often lacks comprehensive benchmarking and frequently depends on proprietary datasets. These limitations hinder reproducibility and may lead to inconsistent or suboptimal model performance. To address these limitations, we introduce the MH-FSF framework, a comprehensive, modular, and extensible platform designed to facilitate the reproduction and implementation of feature selection methods. Developed through collaborative research, MH-FSF provides implementations of 17 methods (11 classical, 6 domain-specific) and enables systematic evaluation on 10 publicly available Android malware datasets. Our results reveal performance variations across both balanced and imbalanced datasets, highlighting the critical need for data preprocessing and selection criteria that account for these asymmetries. We demonstrate the importance of a unified platform for comparing diverse feature selection techniques, fostering methodological consistency and rigor. By providing this framework, we aim to significantly broaden the existing literature and pave the way for new research directions in feature selection, particularly within the context of Android malware detection.
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Copyright (c) 2026 Vanderson Rocha, Diego Kreutz, Hendrio Bragança, Eduardo Feitosa
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