A Grey Wolf Optimizer Feature Selection method and its Effect on the Performance of Document Classification Problem

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Optimization methods are considered as one of the highly developed areas in ArtificialIntelligence (AI). The success of the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA)has encouraged researchers to develop other methods that can obtain better performance outcomes and tobe more responding to the modern needs. The Grey Wolf Optimization (GWO), and the Krill Herd (KH)are some of those methods that showed a great success in different applications in the last few years. In thispaper, we propose a comparative study of using different optimization methods including KH and GWOin order to solve the problem of document feature selection for the classification problem. These methodsare used to model the feature selection problem as a typical optimization method. Due to the complexityand the non-linearity of this kind of problems, it becomes necessary to use some advanced techniques tomake the judgement of which features subset that is optimal to enhance the performance of classificationof text documents. The test results showed the superiority of GWO over the other counterparts using thespecified evaluation measures.