A Feature Selection Based Serial SVM Ensemble Classifier

A serial support vector machine (SVM) ensemble classifier based on feature selection is designed to improve the efficiency and accuracy of unbalanced data classification so as to fully excavate the potential of features. The performance measurement indicators of the traditional SVM model “one-versus-others” are defined. With the goal of reaching higher classification precision and smaller feature set scale, the serial ensemble classifier is built using “one-versus-others” binary SVM classifier as a base classifier, and ant colony optimization (ACO) is used to determine the recognizable category and input feature subset of each level. Several experiments on vibration signals of real states of the engine are performed to contrast the classifying effects of the proposed approach, the traditional “one-versus-one” and “one-versus-others” parallel SVM classifiers, and the results demonstrate the superiority of the proposed approach.