Analog filter circuits feature selection using MRMR and SVM

Abstract

This paper addresses frequency response feature extraction of analog filter circuits. An approach for feature selection using criteria of maximum relevance minimum redundancy (MRMR) and support vector machine (SVM) isproposed. Key idea of the method is to obtain candidate feature subsets with descending order using criteria of MRMR first, and then to select the optimal feature subset through cross validation results of each feature subset using SVM. Experimental results testity that the proposed approach has the advantages of less time-consuming and the selected features have reasonable distribution.

Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System

“…To increase the discriminant potential, those variables that obtained better results with respect to an objective function were selected. A total of four filters were used: Fisher’s score [ 25 , 26 ], ReliefF [ 27 , 28 ], Chi-square [ 29 ], and MRMR [ 30 , 31 ] (see Appendix B for a description of filters ). Applying the four filters , four scores and ranking positions were obtained for each feature.…”

“…To increase the discriminant potential, those variables that obtained better results with respect to an objective function were selected. A total of four filters were used: Fisher's score [25,26], ReliefF [27,28], Chi-square [29], and MRMR [30,31] (see Appendix B for In order to optimize the performance of the algorithms and make their training more efficient, the descriptors were subjected to a normalization process using the z-score method [22][23][24].…”