Parameter-Free Extreme Learning Machine for Imbalanced Classification 
Authors

Li, L - China Agric

Imbalanced data distribution is a common problem in classification situations, that is the number of samples in different categories varies greatly, thus increasing the classification difficulty. Although many methods have been used for the imbalanced data classification, there are still problems with low classification accuracy in minority class and adding additional parameter settings. In order to increase minority classification accuracy in imbalanced problem, this paper proposes a parameter-free weighting learning mechanism based on extreme learning machine and sample loss values to balance the number of samples in each training step. The proposed method mainly includes two aspects: the sample weight learning process based on the sample losses; the sample selection process and weight update process according to the constraint function and iterations. Experimental results on twelve datasets from the KEEL repository show that the proposed method could achieve more balanced and accurate results than other compared methods in this work.

Description

CAUL read and publish agreement 2022

Keywords

Parameter-free, Extreme learning machine, Class imbalance problem, G-mean

Citation

NEURAL PROCESSING LETTERS, 2020, 52 (3), pp. 1927 - 1944

URI

https://hdl.handle.net/10179/17428

Collections

Journal Articles

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