File Entry: Feature Selection Method Using Genetic Algorithm for Classification of Small and High Dimension Data.

 Practical pattern classification problems require selection of a subset of attributes or features to represent the patterns to be classified. The feature selection process is very important which selects the informative features for used classification process. This is due to the fact that performance of the classifier is sensitive to the choice of the features used to construct the good classifier from small or high dimension data that are inherently noisy. In this paper, we propose an efficient feature selection method that finding and selecting informative features from small or high dimension data which maximum the classification accuracy. In this work, we apply genetic algorithm to search out and identify the potential informative features combinations for classification and then use the classification accuracy from the support vector machine classifier to determine the fitness in genetic algorithm. Experimental results with benchmark datasets show the usefulness of the proposed approach for small and high dimension data