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

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

    2 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationIEEE International Conference on Fuzzy Systems
    Pages1084-1089
    Number of pages6
    DOIs
    Publication statusPublished - 2011
    Event2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei
    Duration: 2011 Jun 272011 Jun 30

    Other

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

    Fingerprint

    Fuzzy ARTMAP
    Neural Network Model
    Neural networks
    Adaptive Resonance Theory
    Data Classification
    Data Distribution
    Supervised Learning
    Classification Problems
    Semiconductors
    Data analysis
    Supervised learning
    Adjustment
    Classify
    Industry
    Decay
    Neural Networks
    Metric
    Semiconductor materials
    Learning
    Model

    Keywords

    • Adaptive Resonance Theory Neural Networks
    • Data classification
    • imbalanced data
    • supervised learning

    ASJC Scopus subject areas

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

    Cite this

    Tan, S. C., Watada, J., Ibrahim, Z., Khalid, M., Jau, L. W., & Chew, L. C. (2011). Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models. In IEEE International Conference on Fuzzy Systems (pp. 1084-1089). [6007330] https://doi.org/10.1109/FUZZY.2011.6007330

    Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models. / Tan, Shing Chiang; Watada, Junzo; Ibrahim, Zuwarie; Khalid, Marzuki; Jau, Lee Wen; Chew, Lim Chun.

    IEEE International Conference on Fuzzy Systems. 2011. p. 1084-1089 6007330.

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

    Tan, SC, Watada, J, Ibrahim, Z, Khalid, M, Jau, LW & Chew, LC 2011, Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models. in IEEE International Conference on Fuzzy Systems., 6007330, pp. 1084-1089, 2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011, Taipei, 11/6/27. https://doi.org/10.1109/FUZZY.2011.6007330
    Tan SC, Watada J, Ibrahim Z, Khalid M, Jau LW, Chew LC. Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models. In IEEE International Conference on Fuzzy Systems. 2011. p. 1084-1089. 6007330 https://doi.org/10.1109/FUZZY.2011.6007330
    Tan, Shing Chiang ; Watada, Junzo ; Ibrahim, Zuwarie ; Khalid, Marzuki ; Jau, Lee Wen ; Chew, Lim Chun. / Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models. IEEE International Conference on Fuzzy Systems. 2011. pp. 1084-1089
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