Diversified Ensemble Learning: Integrating Bagging and Teaching–Learning-Based Optimization with a Pairwise Dissimilarity Measure

Ensemble learning has emerged as a powerful technique for improving classification accuracy by combining multiple base models. This study presents an innovative approach to enhance ensemble learning through diversification. The proposed method integrates bagging, a resampling technique, with teaching–learning-based optimization (TLBO), and incorporates a pairwise dissimilarity measure to promote diversity within the ensemble. The TLBO algorithm optimizes the composition of the ensemble by iteratively selecting optimal bags of instances from the training data. The diversity measure quantifies the dissimilarity between bags, ensuring that the ensemble consists of diverse and complementary models. Our proposed model experimented on four benchmarked disease datasets and experimental results demonstrate that the proposed approach achieves superior performance compared to traditional ensemble methods. The ensemble models generated through this approach exhibit improved performance. The proposed model is statistically evaluated using the statistically paired T-test, and the results show our proposed model differs from base models.