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   improving the operation of text categorization systems with selecting proper features based on pso-la  
   
نویسنده rahimirad mozhgan ,mosleh mohammad ,rahmani amir masoud
منبع journal of advances in computer engineering and technology - 2015 - دوره : 1 - شماره : 2 - صفحه:1 -8
چکیده    with the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. one of the major problems in text classification relates to the high dimensional feature spaces. therefore, the main goal of text classification is to reduce the dimensionality of features space. there are many feature selection methods. however, only a few methods are utilized for huge text classification problems. in this paper, we propose a new wrapper method based on particle swarm optimization (pso) algorithm and support vector machine (svm). we combine it with learning automata in order to make it more efficient. to evaluate the efficiency of the proposed method, we compare it with a method which selects features based on genetic algorithm over the reuters-21578 dataset. the simulation results show that our proposed algorithm works more efficiently.
کلیدواژه text mining; feature selection; classification; learning automata(la); particle swarm optimization(pso)
آدرس islamic azad university, ahvaz branch, iran, islamic azad university, dezfool branch, department of computer engineering, iran, islamic azad university, science and research branch, department of computer engineering, iran
پست الکترونیکی rahmani74@yahoo.com; rahmani@srbiau.ac.ir
 
 

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