Title :
Feature Reduction Based on Analysis of Covariance Matrix
Author :
Zhang, Lishi ; Wang, Xianchang ; Qu, Leilei
Author_Institution :
Sch. of Sci., Dalian Fisheries Coll., Dalian, China
Abstract :
This paper presents a novel approach of feature selection based on analysis of covariance matrix of training patterns, a correlation-based feature selection method is put forward. An objective measure is proposed and defined. It is shown that for a given set of features, a subset of features that has the highest sum of the correlation coefficients has the tendency to be reduced, if it meets the requirement of the objective function, a favorable sets is finally retained, when it is omitted, the good classification efficiency is obtained. The algorithm performs elimination, the elimination of which minimizes the value of objective measure, a terminating criterion is given. Experiments show that the proposed algorithm performs well in eliminating irrelevant features while constraining the increase in recognition error rates for unknown data.
Keywords :
covariance matrices; pattern classification; correlation-based feature selection method; covariance matrix analysis; feature reduction; pattern recognition; Aquaculture; Computer science; Covariance matrix; Educational institutions; Feature extraction; Paper technology; Pattern analysis; Performance evaluation; Principal component analysis; Vectors; covariance matrix; feature selection; objective measure;
Conference_Titel :
Computer Science and Computational Technology, 2008. ISCSCT '08. International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3746-7
DOI :
10.1109/ISCSCT.2008.17