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Neural Computing and Applications

Erschienen in:

18.05.2016 | Original Article

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

verfasst von: Yong Zhang, Bo Liu, Jing Cai, Suhua Zhang

Erschienen in: Neural Computing and Applications | Sonderheft 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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Titel: Ensemble weighted extreme learning machine for imbalanced data classification based on differential evolution
verfasst von: Yong Zhang
Bo Liu
Jing Cai
Suhua Zhang
Publikationsdatum: 18.05.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 1/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2342-4

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