Feature selection based on kernel pattern similarity
Yaohua Tang, Jinghuai Gao, Guangzhao Cui · 2008
Reduction of feature dimensionality is of considerable importance in machine learning. The generalization performance of classification system improves when correlated and redundant features are removed. In order to reduce the dimensionality of pattern representation, A new feature election method for support vector machine is proposed. Based on pattern similarity measurement in kernel space, lass separability is deduced and we explore the use of the lass separability in feature selection. The key idea of our ethod is that the feature whose removal downgrades the class separability in kernel space most is relevance to the classification. Experiments on linear and nonlinear synthetic problems and real (world data sets have been (carried out to demonstrate the effectiveness of this method.