Li LZhao KSun RGan JYuan GLiu T2020-122020-12NEURAL PROCESSING LETTERS, 2020, 52 (3), pp. 1927 - 19441370-4621https://hdl.handle.net/10179/17428CAUL read and publish agreement 2022Imbalanced 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.1927 - 1944Parameter-freeExtreme learning machineClass imbalance problemG-meanJournal article10.1007/s11063-020-10282-z4334301573-773XMassey_Dark0801 Artificial Intelligence and Image Processing1702 Cognitive Sciences