Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models

Shing Chiang Tan, Junzo Watada, Zuwarie Ibrahim, Marzuki Khalid, Lee Wen Jau, Lim Chun Chew

    研究成果: Conference contribution

    2 被引用数 (Scopus)

    抄録

    One of the main difficulties in real-world data classification and analysis tasks is that the data distribution can be imbalanced. In this paper, a variant of the supervised learning neural network from the Adaptive Resonance Theory (ART) family, i.e., Fuzzy ARTMAP (FAM) which is equipped with a conflict-resolving facility, is proposed to classify an imbalanced dataset that represents a real problem in the semiconductor industry. The FAM model is combined with the Dynamic Decay Adjustment (DDA) algorithm to form a hybrid FAMDDA network. The classification results of FAM and FAMDDA are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed FAMDDA network in undertaking classification problems with imbalanced datasets.

    本文言語English
    ホスト出版物のタイトルIEEE International Conference on Fuzzy Systems
    ページ1084-1089
    ページ数6
    DOI
    出版ステータスPublished - 2011
    イベント2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei
    継続期間: 2011 6 272011 6 30

    Other

    Other2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011
    CityTaipei
    Period11/6/2711/6/30

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence
    • Applied Mathematics
    • Theoretical Computer Science

    フィンガープリント 「Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

    引用スタイル