Towards Novel Hybrid and Ensemble Instance Selection and Data Re-sampling Approaches for Medical Classification

Class imbalance remains a critical challenge in many real-world classification tasks, particularly within the medical domain, where the accurate identification of minority class instances (e.g., disease cases) is essential. While instance selection (IS) and data re-sampling (DR) have been widely adopted as data-level techniques to address this issue, limited attention has been paid to their integration and ordering effects. This study presents a comprehensive empirical investigation into both single and ensemble-based IS and DR methods, exploring various hybrid combinations and sequencing strategies to improve classification performance on imbalanced medical datasets. We propose and evaluate multiple hybrid workflows, such as hybrid combinations including IS + DR and DR + IS, ensemble combinations including data re-sampling (EDR) and ensemble instance selection (EIS) using union (U), intersection (I) and multi-intersection (MI) strategies, as well as their combined variants (e.g., EDR + EIS, IS + EDR). A total of 22 benchmark medical datasets are examined using five standard classifiers, with performance assessed via AUC and F1 score. Results demonstrate that hybrid and ensemble strategies consistently outperform single-method baselines. In particular, combinations such as Instance Prototypes Filtering (IPF) + EDR-MI and EDR-MI + IPF achieve superior AUC, while IPF + EDR-MI and Edited Nearest Neighbor (ENN) + EDR-MI offer enhanced F1 performance. We also discuss the trade-off between selection aggressiveness, model performance, and computational efficiency. This work provides new insights into the design of effective preprocessing pipelines for imbalanced classification and offers practical guidelines for integrating IS and DR techniques in both standalone and ensemble frameworks.