Web based classification of Tamil documents using ABPA

Automatic text classification based on vector space model (VSM), artificial neural networks (ANN), Knearest neighbor (KNN), N aives Bayes (NB) and support vector machine (SVM) have been applied on English language documents, and gained popularity among text mining and information retrieval (IR) researchers. This paper proposes the application of ANN for the classification of Tamil language documents. Tamil is morphologically rich Dravidian classical language. The development of internet led to an exponential increase in the amount of electronic documents not only in English but also other regional languages. The automatic classification of Tamil documents has not been explored in detail so far. In this paper, corpus is used to construct and test the ANN model. Methods of document representation, assigning weights that reflect the importance of each term are discussed. In a traditional word matching based categorization system, the most popular document representation is VSM. This method needs a high dimensional space to represent the documents. The ANN classifier requires smaller number of features. The experimental results show that ANN model achieves 93.33% using Back Propagation Algorithm (BPA) which is better than the performance of VSM which yields 90.33% on Tamil document classification. In this paper, our goal is to increase the percentage as 94.33% using Advanced Back Propagation Algorithm (ABPA).