An extended fuzzy-kNN approach to solving class-imbalanced problems

Zhigang Xiong*, Junzo Watada, Zhenyuan Xu, Bo Wang, Shing Chiang Tan

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    In this paper, for solving imbalanced classification problem, more attention is placed on data points in the boundary area between two classes. The fuzzy k-nearest neighbors algorithm, which has good performance in conventional classification problems, is adapted here to solve imbalanced classification problems, where G-mean accuracy is used to evaluate our proposal method and compare it with other approaches.

    Original languageEnglish
    Title of host publicationFrontiers in Artificial Intelligence and Applications
    PublisherIOS Press
    Number of pages10
    ISBN (Print)9781614994046
    Publication statusPublished - 2014

    Publication series

    NameFrontiers in Artificial Intelligence and Applications
    ISSN (Print)09226389


    • Fuzzy k-nearest neighbors algorithm
    • Imbalanced dataset classification

    ASJC Scopus subject areas

    • Artificial Intelligence

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