A novel hybrid wrapper-filter approach based on genetic algorithm, particle swarm optimization for feature subset selection

The classification is one of the main technique of machine learning science. In many problems, the data sets have a high dimensionality that the existence of all features is not important to the purpose of the problem, and this will decrease the accuracy and performance of the algorithm. In this situation, the feature selection will play a significant role, and by eliminating unrelated features, the efficiency of the algorithm will be increased. A hybrid filter-wrapper method is proposed in the present study for feature subset selection established with integration of evolutionary based genetic algorithms (GA) and particle swarm optimization (PSO). The presented method mainly aims to reduce the complication of calculation and the search time expended to achieve an optimum solution to the high dimensional datasets feature selection problem. The proposed method, named smart HGP-FS, utilizes artificial neural network (ANN) in the fitness function. The filter and wrapper methods are integrated in order to take the benefit of filter technique acceleration and the wrapper technique vigor for selection of dataset efficacious characteristics. Some dataset characteristics are eliminated through the filter phase, which in turn reduces complex computations and search time in the wrapper phase. Comparisons have been made for the effectiveness of the proposed hybrid algorithm with the usability of three hybrid filter-wrapper methods, two pure wrapper algorithms, two pure filter procedures, and two traditional wrapper feature selection techniques. The findings obtained over real-world datasets show the efficiency of the presented algorithm. The outcomes of algorithm examination on five datasets reveal that the developed method is able to obtain a more accurate classification and to remove unsuitable and unessential characteristics more effectively relative to the other approaches.