Boosting Kernel Discriminant Analysis with Adaptive Kernel Selection

In this paper, we present a new method to enhance classification performance based on Boosting by introducing nonlinear discriminant analysis as feature selection. To reduce the dependency between hypotheses, each hypothesis is constructed in a different feature space formed by Kernel Discriminant Analysis (KDA). Then, these hypotheses are integrated based on AdaBoost. To conduct KDA in each Boosting iteration within realistic time, a new method of kernel selection is also proposed. Several experiments are carried out for the blood cell data and thyroid data to evaluate the proposed method. The result shows that it is almost the same as the best performance of Support Vector Machine without any time-consuming parameter search.