Selecting features from multiple feature sets for SVM committee-based screening of human larynx

This paper is concerned with a two stage procedure for designing a sequential SVM committee and selecting features for the committee from multiple feature sets. It is assumed that features of one type comprise one feature set. Selection of both features and hyper-parameters of SVM classifiers comprising the committee is integrated into one learning process based on genetic search. The designing process focuses on feature selection for pair-wise classification implemented by the SVM. In the first stage, a series of pair-wise SVM are designed starting from the original feature sets as well as from sets created by simple random selection from the original ones. Outputs of the SVM are then converted into probabilities and used as inputs to the second stage SVM. When testing the technique in a three-class classification problem of voice data, a statistically significant improvement in classification accuracy was obtained if compared to parallel committees. The number of feature types and features selected for the pair-wise classification are class specific.