Conference paper
Imbalanced data classification using complementary fuzzy support vector machine techniques and SMOTE
2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2017 (Banff, AB, Canada, 05/10/2017–08/10/2017)
2017
Abstract
A hybrid sampling technique is proposed by combining Complementary Fuzzy Support Vector Machine (CMTFSVM) and Synthetic Minority Oversampling Technique (SMOTE) for handling the imbalanced classification problem. The proposed technique uses an optimised membership function to enhance the classification performance and it is compared with three different classifiers. The experiments consisted of four standard benchmark datasets and one real world data of plant cells. The results revealed that implementing CMTFSVM followed by SMOTE provided better result over other FSVM classifiers for the benchmark datasets. Furthermore, it presented the best result on real world dataset with 0.9589 of G-mean and 0.9598 of AUC. It can be concluded that the proposed techniques work well with imbalanced benchmark and real world data.
Details
- Title
- Imbalanced data classification using complementary fuzzy support vector machine techniques and SMOTE
- Authors/Creators
- R. Pruengkarn (Author/Creator)K.W. Wong (Author/Creator)C.C. Fung (Author/Creator)
- Publication Details
- 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
- Conference
- IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2017 (Banff, AB, Canada, 05/10/2017–08/10/2017)
- Identifiers
- 991005540443807891
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Conference paper
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