Selection of Wavelet Features for Biomedical Signals Using SVM Learning
Girisha Garg, Vijander Singh · Advances in data mining and database management book series · 2016
Signal processing problems require feature extraction and selection techniques. A novel Wavelet Feature Selection algorithm is proposed for ranking and selecting the features from the wavelet decompositions. The algorithm makes use of support vector machine to rank the features and backward feature elimination to remove the features. The finally selected features are used as patterns for the classification system. Two EEG datasets are used to test the algorithm. The results confirm that the algorithm is able to improve the efficiency of wavelet features in terms of accuracy and feature space.