Class-Specific Features Using J48 Classifier for Text Classification

In the field of information retrieval text categorization is the key research area in present. The text categorization selects entries from set of pre built categories and allots those to a document. Learning with high dimensional data space is challenging in a text categorization method. Learning with high-dimensional features may prompt a heavy calculation overhead and may affect the classification performance of classifiers because of unrelated and repetitive features. To improve the “scourge of dimensionality” issue and to accelerate the learning procedure of classifiers, it is important to perform feature reduction to reduce the size of features. This paper introduces a Bayesian arrangement approach and J48 classifier for auto text categorization using class-specific features. For text classification, the proposed strategy selects a specific feature subset for every class. In contribution J48 classifier combining with term weighting concept as weighted j48 classifier is used for classification. These methods increase the accuracy of classification and feature selection process and improve the system performance. The detectable importance of this methodology is that many feature selection criteria can be easily used. The weighted J48 classifier saves both the time and memory. The proposed system also uses Term weighting concept for preprocessing. These methods increase the accuracy of classification and feature selection process and improve the system performance.