Optimizing Weighted Extreme Learning Machines for imbalanced classification and application to credit card fraud detection

Document Type

Article

Publication Date

9-24-2020

Abstract

The classification problems with imbalanced datasets widely exist in real word. An Extreme Learning Machine is found unsuitable for imbalanced classification problems. This work applies a Weighted Extreme Learning Machine (WELM) to handle them. Its two parameters are found to affect its performance greatly. The aim of this work is to apply various intelligent optimization methods to optimize a WELM and compare their performance in imbalanced classification. Experimental results show that WELM with a dandelion algorithm with probability-based mutation can perform better than WELM with improved particle swarm optimization, bat algorithm, genetic algorithm, dandelion algorithm and self-learning dandelion algorithm. In addition, the proposed algorithm is applied to credit card fraud detection. The results show that it can achieve high detection performance.

Identifier

85085273720 (Scopus)

Publication Title

Neurocomputing

External Full Text Location

https://doi.org/10.1016/j.neucom.2020.04.078

e-ISSN

18728286

ISSN

09252312

First Page

50

Last Page

62

Volume

407

Grant

113052015KJ05

Fund Ref

Alexander von Humboldt-Stiftung

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