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   A Comparative Study of the Ensemble and Base Classifiers Performance in Malay Text Categorization  
   
نویسنده Alshalabi Hamood Ali ,Tiun Sabrina ,Omar Nazlia
منبع Asia-Pacific Journal Of Information Technology And Multimedia - 2017 - دوره : 6 - شماره : 2 - صفحه:53 -64
چکیده    Automatic text categorization (atc) has attracted the attention of the research community over the last decade as it frees organizations from the need of manually organized documents. the ensemble techniques, which combine the results of a number of individually trained base classifiers, always improve classification performance better than base classifiers. this paper intends to compare the effectiveness of ensemble with that of base classifiers for malay text classification. two feature selection methods (the gini index (gi) and chi-square) with the ensemble methods are applied to examine malay text classification, with the intention to efficiently integrate base classifiers algorithms into a more accurate classification procedure. two types of ensemble methods, namely the voting combination and meta-classifier combination, are evaluated. a wide range of comparative experiments are conducted to assess classified malay dataset. the applied experiments reveal that meta-classifier ensemble framework performed better than the best individual classifiers on the tested datasets.
کلیدواژه Feature Selection. Individual Classifiers. Ensemble Classifiers. Malay Text Classification
آدرس Universiti Kebangsaan Malaysia, Faculty Of Information Science And Technology, Malaysia, Universiti Kebangsaan Malaysia, Faculty Of Information Science And Technology, Malaysia, Universiti Kebangsaan Malaysia, Faculty Of Information Science And Technology, Malaysia
 
     
   
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