HiBBKA: A Hybrid Method With Resampling and Heuristic Feature Selection for Class-Imbalanced Data in Chemometrics

Abstract

In critical domains including medicinal chemistry, biomedicine, metabolomics, and computational toxicology, class imbalance in datasets and poor recognition accuracy for minority classes remain persistent challenges. While previous studies have employed resampling and feature selection techniques to address data imbalance and enhance classification performance, most approaches have focused on single-algorithm solutions rather than hybrid methodologies. Hybrid algorithms offer distinct advantages by integrating the strengths of multiple techniques, thereby providing more comprehensive and efficient solutions for handling imbalanced data. This study proposes HiBBKA, a novel hybrid algorithm combining radial-based under-sampling with SMOTE (RBU-SMOTE) and an improved binary black-winged kite algorithm (iBBKA) for feature selection. The proposed framework operates through two key phases: First, the RBU-SMOTE resampling method synergistically integrates radial-based under-sampling (RBU) with the synthetic minority oversampling technique (SMOTE), effectively addressing class-imbalance distribution while enhancing the quality of synthesized samples. Second, the enhanced iBBKA feature selection algorithm systematically identifies the most discriminative features critical for classification tasks. We comprehensively evaluate RBU-SMOTE and HiBBKA using multiple classifiers across 16 imbalanced datasets, including real-world medical datasets, with particular emphasis on the minority class performance. Experimental results demonstrate that RBU-SMOTE achieves competitive performance compared to existing resampling methods, while the complete HiBBKA framework significantly outperforms state-of-the-art algorithms in overall classification metrics, particularly in the minority class recognition.