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Classification of imbalanced data by combining the complementary neural network and SMOTE algorithm

Jeatrakul, P., Wong, K.W. and Fung, C.C. (2010) Classification of imbalanced data by combining the complementary neural network and SMOTE algorithm. In: 17th International Conference on Neural Information Processing, ICONIP 2010, 22 - 25 November, Sydney pp. 152-159.

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    Link to Published Version: http://dx.doi.org/10.1007/978-3-642-17534-3_19
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    Abstract

    In classification, when the distribution of the training data among classes is uneven, the learning algorithm is generally dominated by the feature of the majority classes. The features in the minority classes are normally difficult to be fully recognized. In this paper, a method is proposed to enhance the classification accuracy for the minority classes. The proposed method combines Synthetic Minority Over-sampling Technique (SMOTE) and Complementary Neural Network (CMTNN) to handle the problem of classifying imbalanced data. In order to demonstrate that the proposed technique can assist classification of imbalanced data, several classification algorithms have been used. They are Artificial Neural Network (ANN), k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM). The benchmark data sets with various ratios between the minority class and the majority class are obtained from the University of California Irvine (UCI) machine learning repository. The results show that the proposed combination techniques can improve the performance for the class imbalance problem.

    Publication Type: Conference Paper
    Murdoch Affiliation: School of Information Technology
    Publisher: Springer-Verlag
    Copyright: © 2010 Springer-Verlag.
    Notes: In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 6444 LNCS, Issue PART 2, 2010, Pages 152-159
    URI: http://researchrepository.murdoch.edu.au/id/eprint/3630
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