Conference paper
Handling skewed imbalanced data using Complementary Fuzzy Support Vector Machine and SMOTE
7th International Symposium on Computational Intelligence and Industrial Applications, (ISCIIA) 2016 (Beijing Institute of Technology (BIT), Beijing, China, 03/11/2016–06/11/2016)
2016
Abstract
A hybrid sampling technique is proposed by combining Complementary Fuzzy Support Vector Machine (CMTFSVM) and Synthetic Minority Over-sampling Technique (SMOTE) for handling imbalanced classification problem. The proposed technique is compared with three different classifiers. The optimize membership function is chosen to enhance the classification performance. The experiment uses the Glass5 dataset that has 22.78% of imbalanced ratio. The results revealed that by implementing CMTFSVM first and then applying SMOTE provided the best performance over the other methods with 0.9638 of G-mean and 0.9646 of AUC.
Details
- Title
- Handling skewed imbalanced data using Complementary Fuzzy Support Vector Machine and SMOTE
- Authors/Creators
- R. Pruengkarn (Author/Creator)K.W. Wong (Author/Creator)C.C. Fung (Author/Creator)Y. Takama (Author/Creator)
- Conference
- 7th International Symposium on Computational Intelligence and Industrial Applications, (ISCIIA) 2016 (Beijing Institute of Technology (BIT), Beijing, China, 03/11/2016–06/11/2016)
- Identifiers
- 991005542983107891
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Conference paper
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