Title :
A method based on weighted F-score and SVM for feature selection
Author :
Peng Tao ; Huang Yi ; Cao Wei ; Lou Yang Ge ; Liang Xu
Author_Institution :
Sch. of Inf. Sci. & Eng., Central South Univ., Changsha, China
Abstract :
A novel feature selection method based on weighted F-score and SVM is proposed for the problems which inter-class overlapping and consistency of the features are ignored in traditional F-score feature selection method. Firstly, overlapping weight and consistency weight are introduced. Secondly, F-score value of every feature is calculated. Thirdly, F-score values of the features are ordered from high to low. Finally, the feature set of optimal recognition performance is selected by SVM. Simulation results for UCI machine learning database and experimental results for froth floatation plant data demonstrate that the proposed method can achieve better performance than traditional methods and has a good ability for generalization.
Keywords :
feature extraction; flotation (process); generalisation (artificial intelligence); learning (artificial intelligence); pattern recognition; production engineering computing; statistical analysis; support vector machines; F-score value; SVM based method; UCI machine learning database; feature consistency; feature selection method; froth floatation plant data; generalization; interclass feature overlapping; optimal recognition performance; weighted F-score based method; Accuracy; Databases; Educational institutions; Pattern recognition; Support vector machines; Testing; Training; Consistency Weight; Overlapping Weight; Weighted F-score;
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
DOI :
10.1109/CCDC.2013.6561705
