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Ensemble weighted extreme learning machine for imbalanced data classification based on differential evolution
Authors:Zhang  Yong  Liu  Bo  Cai  Jing  Zhang  Suhua
Affiliation:1.School of Computer and Information Technology, Liaoning Normal University, No. 1, Liushu South Street, Ganjingzi District, Dalian, 116081, Liaoning Province, China
;2.State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China
;
Abstract:

Extreme learning machine for single-hidden-layer feedforward neural networks has been extensively applied in imbalanced data learning due to its fast learning capability. Ensemble approach can effectively improve the classification performance by combining several weak learners according to a certain rule. In this paper, a novel ensemble approach on weighted extreme learning machine for imbalanced data classification problem is proposed. The weight of each base learner in the ensemble is optimized by differential evolution algorithm. Experimental results on 12 datasets show that the proposed method could achieve more classification performance compared with the simple vote-based ensemble method and non-ensemble method.

Keywords:
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