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International Conference on Neural Information Processing

ICONIP 2017: Neural Information Processing pp 67–75Cite as

Multiclass Imbalanced Classification Using Fuzzy C-Mean and SMOTE with Fuzzy Support Vector Machine

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10638))

Abstract

A hybrid sampling technique is proposed by combining Fuzzy C-Mean Clustering and Synthetic Minority Oversampling Technique (FCMSMT) for tackling the imbalanced multiclass classification problem. The mean number of classes is used as the number of instances for applying undersampling and oversampling. Using the mean as the fixed number of the required instances for each class can prevent the within-class imbalance data from being eliminated erroneously during undersampling. This technique can decrease both within-class and between-class errors, and thus can increase the classification performance. The study was conducted using eight benchmark datasets from KEEL and UCI repositories and the results were compared against three major classifiers based on G-mean and AUC measurements. The results reveal that the proposed technique could handle most of the multiclass imbalanced datasets used in the experiments for all classifiers and retain the integrity of the original data.

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Authors and Affiliations

  1. School of Engineering and Information Technology, Murdoch University, Perth, Australia

    Ratchakoon Pruengkarn, Kok Wai Wong & Chun Che Fung

Authors
  1. Ratchakoon Pruengkarn
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  2. Kok Wai Wong
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  3. Chun Che Fung
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Corresponding author

Correspondence to Ratchakoon Pruengkarn .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Pruengkarn, R., Wong, K.W., Fung, C.C. (2017). Multiclass Imbalanced Classification Using Fuzzy C-Mean and SMOTE with Fuzzy Support Vector Machine. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-70139-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70138-7

  • Online ISBN: 978-3-319-70139-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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