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

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

    研究成果: Conference contribution

    抄録

    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.

    本文言語English
    ホスト出版物のタイトルFrontiers in Artificial Intelligence and Applications
    出版社IOS Press
    ページ200-209
    ページ数10
    262
    ISBN(印刷版)9781614994046
    DOI
    出版ステータスPublished - 2014

    出版物シリーズ

    名前Frontiers in Artificial Intelligence and Applications
    262
    ISSN(印刷版)09226389

    ASJC Scopus subject areas

    • 人工知能

    引用スタイル