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Neural Computing and Applications

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18-05-2016 | Original Article

Ensemble weighted extreme learning machine for imbalanced data classification based on differential evolution

Authors: Yong Zhang, Bo Liu, Jing Cai, Suhua Zhang

Published in: Neural Computing and Applications | Special Issue 1/2017

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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.

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Title: Ensemble weighted extreme learning machine for imbalanced data classification based on differential evolution
Authors: Yong Zhang
Bo Liu
Jing Cai
Suhua Zhang
Publication date: 18-05-2016
Publisher: Springer London
Published in: Neural Computing and Applications / Issue Special Issue 1/2017
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2342-4

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