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

Pruengkarn, R., Wong, K.W. and Fung, C.C. (2017) Multiclass Imbalanced Classification Using Fuzzy C-Mean and SMOTE with Fuzzy Support Vector Machine. Lecture Notes in Computer Science, 10638 . pp. 67-75.

Link to Published Version: https://doi.org/10.1007/978-3-319-70139-4_7
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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.

Publication Type: Journal Article
Murdoch Affiliation: School of Engineering and Information Technology
Publisher: Springer Verlag
Copyright: © 2017 Springer International Publishing AG
URI: http://researchrepository.murdoch.edu.au/id/eprint/39788
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