An Improved Support Vector Machine for the Classification of Imbalanced Biological Datasets

Authors:
Haiying Wang;Huiru Zheng
Affiliations:
School of Computing and Mathematics, University of Ulster, Newtownabbey, Co. Antrim, UK BT37 0QB;School of Computing and Mathematics, University of Ulster, Newtownabbey, Co. Antrim, UK BT37 0QB
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Year:
2008

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Abstract

Based on advances in statistical learning theory, Support Vector Machine (SVM) has demonstrated unique features and state-of-the-art performance in many real-world classification problems. However, conventional SVM utilizes a sign function to classify test data into different classes, which has shown some limitations that hinder its performance. This paper exploresthe feasibility of incorporating information theory-based approaches into SVM decision making process and demonstrated its application in the classification of imbalanced biological datasets. The results obtained indicated that by incorporating information theory-based technique, a significant improvement was achieved (p