Kernel Feature Selection to Improve Generalization Performance of Boosting Classifiers

In this paper, kernel feature selection is proposed to improve generalization performance of boosting classifiers. Kernel feature Selection attains the feature selection and model selection at the same time using a simple selection algorithm. The algorithm automatically selects a subset of kernel features for each classifier and combines them according to theLogitBoostalgorithm. The system employs kernel logistic regression for the base-learner, and a kernel feature is selected at each stage of boosting to improve the generalization error. The proposed method was applied to the MIT CBCL pedestrian image database, and kernel features were extracted from each pixel of the images as a local feature. The experimental results showed good generalization error with local feature selection, while more improvement was achieved with the kernel feature selection.