Affiliations: [a] School of Computer and Information Technology, Liaoning Normal University, Dalian, China
| [b] State Key Lab. for Novel Software Technology, Nanjing University, Nanjing, China
Correspondence:
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Corresponding author. Yong Zhang, School of Computer and Information Technology, Liaoning Normal University, No. 1, Liushu South Street, Ganjingzi District, Dalian 116081, Liaoning Province, China. E-mail: zhyong@lnnu.edu.cn.
Abstract: This paper proposes an evolutionary-based selective ensemble learning framework for solving classification problem. In the proposed ensemble learning framework, extreme learning machine (ELM) is selected as base learner and evolutionary algorithms are employed to optimize the weights of base learners in the ensemble. Then, some base learners, that their weights are larger than the threshold, are selected for making decision. The proposed ensemble learning framework is evaluated on 20 benchmark data sets from KEEL repository through four different evolutionary algorithms. Results show that the proposed evolutionary-based ensemble learning framework outperforms the simple voting based ensemble method in terms of classification performance. In four evolutionary optimization algorithms, PSOGA-based and DE-based weight optimization algorithms can effectively improve the classification accuracy and generalization ability.
