Differential Evolution Based Selective Ensemble of Extreme Learning Machine

Abstract

Extreme learning machine (ELM) for single-hidden-layer feedforward neural networks has been widely used in classification and regression for its fast learning speed. However, a single ELM suffers from problems of stability and overfitting. Ensemble approach can effectively resolve these problems. This paper proposes a selective ensemble learning algorithm based on differential evolution (DE) for classification problem. In the proposed algorithm, ELM is selected as base classifier, and then DE algorithm is employed as the optimization technique to construct an ensemble learning model by combining base classifiers. The weights of each base classifier in the ensemble are optimized by DE algorithm. Finally, several base classifiers with larger weights are selected to form the ensemble for making decision. Experimental results on 14 benchmark datasets demonstrate that the proposed algorithm can effectively improve the classification accuracy and generalization ability.