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The Scientific World Journal
Volume 2014 (2014), Article ID 536434, 12 pages
Research Article

New Fuzzy Support Vector Machine for the Class Imbalance Problem in Medical Datasets Classification

School of Information Science and Engineering, Changzhou University, Changzhou 213164, China

Received 25 November 2013; Accepted 20 February 2014; Published 23 March 2014

Academic Editors: V. Bhatnagar and Y. Zhang

Copyright © 2014 Xiaoqing Gu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In medical datasets classification, support vector machine (SVM) is considered to be one of the most successful methods. However, most of the real-world medical datasets usually contain some outliers/noise and data often have class imbalance problems. In this paper, a fuzzy support machine (FSVM) for the class imbalance problem (called FSVM-CIP) is presented, which can be seen as a modified class of FSVM by extending manifold regularization and assigning two misclassification costs for two classes. The proposed FSVM-CIP can be used to handle the class imbalance problem in the presence of outliers/noise, and enhance the locality maximum margin. Five real-world medical datasets, breast, heart, hepatitis, BUPA liver, and pima diabetes, from the UCI medical database are employed to illustrate the method presented in this paper. Experimental results on these datasets show the outperformed or comparable effectiveness of FSVM-CIP.