To solve the problem of imbalanced data classification, a resampling algorithm based on sample similarity and variation coefficient is proposed. For minority samples, new samples are synthesized with bias according to the similarity between samples. For majority samples, the boundary region is determined according to their variation coefficients, and the samples in the non-boundary region are deleted randomly. By the above two methods, original data set is reconstructed, and sample scales of two classes become more balanced. The identification ability of minority samples and overall classification accuracy can be improved effectively by scientifically guiding oversampling and undersampling. Finally, the imbalanced data sets were adopted for verification by using the kernel extreme learning machine (KELM) as classifier. Experimental results show that the overall performance of the proposed algorithm is superior to other comparison algorithms in dealing with imbalanced data classification.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Resampling algorithm based on sample similarity and variation coefficient


    Contributors:
    Jiafeng, Zhu (author) / Ruifeng, Li (author) / Xia, Chen (author) / Gui, Chen (author)


    Publication date :

    2022-10-12


    Size :

    1215156 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Particle filter based on improved genetic algorithm resampling

    Wang, W. / Tan, Q. K. / Chen, J. et al. | IEEE | 2016




    Interacting multiple model particle filter optimization resampling algorithm

    Zhou, Weidong / Sun, Tian / Chu, Min et al. | British Library Online Contents | 2017


    A New Parallel Resampling Algorithm for GPU-Accelerated Particle Filter

    Hong, Kyung Woo / Kim, Youngjoo / Bang, Hyochoong | AIAA | 2023