English

Abstract

Text categorization is the task of deciding whether a document belongs to a set of pre specified classes of documents. Automatic classification schemes can greatly facilitate the process of categorization. Categorization of documents is challenging, as the number of discriminating words can be very large. Many existing algorithms simply would not work with these many numbers of features. For most text categorization tasks, there are many irrelevant and many relevant features. The main objective is to propose a text classification based on the features selection and pre-processing thereby reducing the dimensionality of the Feature vector and increase the classification accuracy. In the proposed method, machine learning methods for text classification is used to apply some text preprocessing methods in different dataset, and then to extract feature vectors for each new document by using various feature weighting methods for enhancing the text classification accuracy. Further training the classifier by Naive Bayesian (NB) and K-nearest neighbor (KNN) algorithms, the predication can be made according to the category distribution among this k nearest neighbors. Experimental results show that the methods are favorable in terms of their effectiveness and efficiency when compared with other.