A hybrid learning algorithm for text classification

Details A hybrid learning algorithm for text classification A hybrid learning algorithm for text classification

2010-09-23

arxiv.org/abs/1009.4574v1

Proc. 3rd International Conference on Electrical & Computer Engineering (ICECE 2004), Dhaka Bangladesh, pp. 577-580, Dec. 2004

Computer Science

Neural and Evolutionary Computing

4 pages, International Conference

Scientific paper

Text classification is the process of classifying documents into predefined categories based on their content. Existing supervised learning algorithms to automatically classify text need sufficient documents to learn accurately. This paper presents a new algorithm for text classification that requires fewer documents for training. Instead of using words, word relation i.e association rules from these words is used to derive feature set from preclassified text documents. The concept of Naive Bayes classifier is then used on derived features and finally only a single concept of Genetic Algorithm has been added for final classification. Experimental results show that the classifier build this way is more accurate than the existing text classification systems.

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