关键词: 差分进化, 极限学习机, 集成分类, 加权

Abstract: Ensemble classification can effectively improve the classification performance by combining several weak classifiers according to a certain rule. In the process of combination, the importance of each weak classifier to the classification result is often different. Extreme learning machine is a new learning algorithm for training single-hidden-layer feedforward neural networks. In this paper, the extreme learning machine is selected as a base classifier, and a weighted ensemble method based on differential evolution is proposed. The proposed method optimizes the weights of each base classifier in the ensemble method based on differential evolution algorithm. Experimental results show that the proposed method has higher classification accuracy and better generalization ability compared with the simple-voting method and Adaboost ensemble method.

Key words: differential evolution, extreme learning machine, ensemble classification, weighting