earticle

영어

Classifying cork stopper into group required large set of visual features. Selecting an optimal feature subset from large input feature set speeds up classification task and improve the classifier accuracy. Traditional feature selection methods, such as sequential forward selection, sequential backward selection, and sequential forward floating search are costly to implement. This paper we propose a feature selection method known as principal feature analysis that exploits the structure of the principal components of a feature set to find a subset of the original features information and support vector machines (SVMs) for classification. The experimental result show that the proposed method for SVM based classifier is lot faster than PCA and ICA based methods. It is also leads to better performance when the same number of principal/independent components is used and consistently picks the best subset of features in terms of sum-squared-error compared to competing methods.