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

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

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

    Abstract

    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
    Pages200-209
    Number of pages10
    Volume262
    ISBN (Print)9781614994046
    DOIs
    Publication statusPublished - 2014

    Publication series

    NameFrontiers in Artificial Intelligence and Applications
    Volume262
    ISSN (Print)09226389

    Keywords

    • Fuzzy k-nearest neighbors algorithm
    • Imbalanced dataset classification

    ASJC Scopus subject areas

    • Artificial Intelligence

    Cite this

    Xiong, Z., Watada, J., Xu, Z., Wang, B., & Tan, S. C. (2014). An extended fuzzy-kNN approach to solving class-imbalanced problems. In Frontiers in Artificial Intelligence and Applications (Vol. 262, pp. 200-209). (Frontiers in Artificial Intelligence and Applications; Vol. 262). IOS Press. https://doi.org/10.3233/978-1-61499-405-3-200