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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2011 Vol.2(6): 741-745 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.227

A Comparative Study of Improved F-Score with Support Vector Machine and RBF Network for Breast Cancer Classification

P. Jaganathan, N. Rajkumar, and R. Kuppuchamy
Abstract—Feature selection is an important issue in classification of cancer diagnosis. In this paper, a new feature selection method, named improved F-Score is applied for breast cancer diagnosis. First, the improved F-Score values of all the features are calculated using improved F-Score formula. Then the mean value is computed for the calculated improved F-Score values. The improved F-Score values which are greater than the mean improved F-Score are selected. Wisconsin breast cancer dataset (WBCD) is used in this study. As classification algorithms, Support Vector Machine and RBF Network are sued. The results obtained from improved F-Score with Support Vector Machines have produced efficient results compared to improved F-Score with RBF Network. Therefore we show that improved F-Score combined with promising than improved F-Score with RBF Network.

Index Terms—Breast cancer, feature selection, improved F-Score, RBF network, SVM.

The authors are with Department of Computer Applications, PSNA College of Engineering & Technology, Dindigul, Tamilnadu, India ( email:rknpsna@gmail.com)

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Cite:P. Jaganathan, N. Rajkumar, and R. Kuppuchamy, "A Comparative Study of Improved F-Score with Support Vector Machine and RBF Network for Breast Cancer Classification," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 741-745, 2012.

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