Improved support vector machine generalization using normalized input space

Authors:
Shawkat Ali;Kate A. Smith-Miles
Affiliations:
School of Information Systems, Central Queensland University, QLD, Australia;School of Engineering and Information Technology, Deakin University, VIC, Australia
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

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Abstract

Data pre-processing always plays a key role in learning algorithm performance. In this research we consider data pre-processing by normalization for Support Vector Machines (SVMs). We examine the normalization affect across 112 classification problems with SVM using the rbf kernel. We observe a significant classification improvement due to normalization. Finally we suggest a rule based method to find when normalization is necessary for a specific classification problem. The best normalization method is also automatically selected by SVM itself.