Classification of unbalanced problems based on improved weighted extreme learning machine

Chenlong Guo; Pu Wang; Haoxiang Luo

doi:10.1117/12.2514827

29 October 2018 Classification of unbalanced problems based on improved weighted extreme learning machine

Chenlong Guo, Pu Wang, Haoxiang Luo

Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 108361T (2018) https://doi.org/10.1117/12.2514827
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China

Abstract

When the traditional extreme learning machine is dealing with unbalanced data sets, the classification effect of the small number of samples is not ideal. A weighted extreme learning machine based on KFCM is proposed for this problem, and different penalty factors are given according to the proportion of samples in different categories.At the same time, considering the impact of outliers, the KFCM clustering gets the degree of membership that each type of sample belongs to, and adopts the degree of membership to conduct quadratic weighted means on penalty factors of extreme learning machine. Due to the high cost of calculating the generalized inverse of the weighted extreme learning machine, a method of cholesky decomposition is proposed. The simulation test results of the UCI standard datasets show that the proposed algorithm not only effectively improves the classification accuracy of the minority samples, but also achieves the optimal performance in the F-measure and G-means indexes, and the computation speed is much faster than the ordinary extreme learning machine algorithm.

Citation Download Citation

Chenlong Guo, Pu Wang, and Haoxiang Luo "Classification of unbalanced problems based on improved weighted extreme learning machine", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108361T (29 October 2018); https://doi.org/10.1117/12.2514827

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