Boosted-PCA for binary classification problems
Seaung Lok Ham, Nojun Kwak · 2012
In this paper, a Boosted-PCA algorithm is proposed for efficient classification of two class data. Conventionally, in classification problems, the roles of feature extraction and classification have been distinct, i.e., a feature extraction method and a classifier are applied sequentially to classify input variable into several categories. In this paper, these two steps are combined into one resulting in a good classification performance. More specifically, each principal component is treated as a weak classifier in Adaboost algorithm to constitute a strong classifier for binary classification problems. The proposed algorithm is applied to UCI data set and showed better recognition rates than sequential application of feature extraction and classification methods such as PCA+1NN and PCA+SVM.