A two-step supervised learning artificial neural network for imbalanced dataset problems

Asrul Adam*, Zuwairie Ibrahim, Mohd Ibrahim Shapiai, Lim Chun Chew, Lee Wen Jau, Marzuki Khalid, Junzo Watada

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    13 Citations (Scopus)


    In this paper, a two-step supervised learning algorithm of a single layer feedforward Artificial Neural Network (ANN) is proposed for solving imbalanced dataset problems. Levenberg Marquart backpropagation learning algorithm is utilized in the first step learning, while the second step learning mechanism is introduced by optimizing the decision threshold of the step function at the output layer of ANN using particle swarm optimization (PSO). After all the steps learning are accomplished, the best weights and decision threshold value are obtained to be used for testing process. Several imbalanced datasets, which are available in UCI Machine Learning Repository, are chosen as case study. The prediction performance is assessed by Geometric Mean (G-mean), which is a standard measure to indicate the efficiency of classifier for imbalanced datasets. Based on the experimental results, the proposed method is able to provide good G-mean value compared with the conventional ANN approaches.

    Original languageEnglish
    Pages (from-to)3163-3172
    Number of pages10
    JournalInternational Journal of Innovative Computing, Information and Control
    Issue number5 A
    Publication statusPublished - 2012 May


    • Articial neural network
    • Decision threshold
    • Imbalanced dataset problem
    • Machine learning
    • Particle swarm opti-mization
    • Single layer feedforward neural network
    • Two-class classication

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Information Systems
    • Software
    • Theoretical Computer Science


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