Heuristic parallel selective ensemble algorithm based on clustering and improved simulated annealing

Abstract

Ensemble learning can achieve stronger generalization ability by training multiple weak classifier models and fusing the prediction results of these weak classifiers. However, with the increase in the number of weak classifier models, the complexity and training time of the model are also increasing. This paper introduces a heuristic parallel selective ensemble algorithm based on clustering and improved simulated annealing. First, the method uses selective clustering to remove similar weak classifiers, so as to reduce the number of weak models, and select the weak classifiers with large differences as candidate sets. Then, the optimal classifier sequence set is selected based on the improved simulated annealing heuristic selective ensemble algorithm, which improves the classification performance of the model. Moreover, the parallel integration strategy is adopted to improve the efficiency of selecting optimal subset of classifiers, thus effectively reducing the training time of the model. Experimental results show that the proposed algorithm exhibits better performance compared with traditional methods.