An optimal feature subset selection using GA for leaf classification

This paper describes an optimal approach for feature extraction and selection for classification of leaves based on Genetic Algorithm (GA). The selection of the optimal features subset and the classification has become an important methodology in the field of Leaf classification. The deterministic feature sequence is extracted from the leaf images using GA technique, and these extracted features are further used to train the Support Vector Machine (SVM). GA is applied to optimize the features of color and boundary sequences, and to improve the overall generalization performance based on the matching accuracy. SVM is applied to produce the false positive and false negative features. Our experimental results indicate that the application of GA for feature subset selection using SVM as a classifier proves computationally effective and improves the accuracy compared to KNN to classify the leaf patterns.