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Classification of unbalanced problems based on improved weighted extreme learning machine
Author(s): Chenlong Guo; Pu Wang; Haoxiang Luo
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Paper 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.

Paper Details

Date Published: 29 October 2018
PDF: 8 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108361T (29 October 2018); doi: 10.1117/12.2514827
Show Author Affiliations
Chenlong Guo, Science and Technology on Electro-optic Control Lab. (China)
Luoyang Institute of Electro-Optical Equipment (China)
Pu Wang, Shanghai Advanced Research Institute (China)
Haoxiang Luo, Univ. of Electronic Science and Technology of China (China)


Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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