Filter-wrapper approach to feature selection of GPCR protein

Protein dataset contains high dimensional feature space. These features may encompass of noise and not relatively to protein function. Therefore, we need to select the appropriate features to improve the efficiency and performance of the classifier. Feature selection is an important step in any classification tasks. Filter methods are important in order to obtain only the relevant features to the class and to avoid redundancy. While wrapper methods are applied to get optimized features and better classification accuracy. This paper proposed a feature selection strategy for hierarchical classification of G-Protein-Coupled Receptors (GPCR) based on hybridization of correlation feature selection (CFS) filter and genetic algorithm (GA) wrapper methods. The optimum features were then classified using K-nearest neighbor algorithm. These methods are capable to reduce the features and achieved comparable classification accuracy at every hierarchy level. The results also shown that the integration between CFS and GA is capable of searching the optimum features for hierarchical protein classification.