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 < 0.005), especially in the process of classification of imbalanced datasets. The proposed methodology not only can improve the overall prediction performance but also can make the classification with the SVM less sensitive to the selection of input parameters.
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Wang, H., Zheng, H. (2008). An Improved Support Vector Machine for the Classification of Imbalanced Biological Datasets. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_9
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DOI: https://doi.org/10.1007/978-3-540-87442-3_9
Publisher Name: Springer, Berlin, Heidelberg
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