Ensemble Instance Selection for Medical Datasets: Single‐Stage Parallel and Two‐Stage Serial Ensemble Approaches

ABSTRACT Instance selection is an important data preprocessing step in data mining that removes noisy data from training datasets to build more effective models and reduces dataset size to improve computational efficiency during model training. Traditional instance selection algorithms have varying strengths and weaknesses, making it difficult for any single method to consistently achieve optimal performance across diverse datasets. Therefore, in this paper, two novel ensemble instance selection methods are proposed, namely Single‐Stage Parallel Ensemble (SSPE) and Two‐Stage Serial Ensemble (TSSE), which are based on combining multiple instance selection algorithms in the parallel and serial manners. The proposed ensemble approaches aim to improve data reduction and classification performance. Extensive experiments are conducted on 17 medical datasets of varying sizes, evaluating four instance selection algorithms, CNN, ENN, IPF, and GA, alongside three classifiers: KNN, SVM, and RF. Results demonstrate that certain SSPE combinations, particularly the union of ENN and IPF, outperform single baseline algorithms in classification accuracy and AUC while maintaining effective data reduction. Although TSSE achieves higher reduction rates, its classification performance is inferior to SSPE. Overall, the proposed methods serve as effective preprocessing tools for medical data and provide a strong baseline for future ensemble instance selection research.