N-Gram and KLD Based Efficient Feature Selection Approach for Text Categorization
| Author(s) | : | Smita Shedbale, Dr. Kailash Shaw |
| Institution | : | ME Student, Department of Computer Engineering, DYPCOE, Akurdi, SPPU, Pune, India |
| Published In | : | Vol. 4, Issue 6 — June 2017 |
| Page No. | : | 660-668 |
| Domain | : | Engineering |
| Type | : | Research Paper |
| ISSN (Online) | : | 2348-4470 |
| ISSN (Print) | : | 2348-6406 |
Automated categorization of text into a set of predermined categories has become one of the importantapproach for handling and organizing enormous amount of web document. Text categorization method is used in a widevariety of applications such as news article categorization, spam filtering. To deal with a major challenge in textcategorization that is high dimensionality of the feature space, feature extraction and feature selection plays mostimportant role. In this paper, TF N-gram based and unigram TF-IDF based method will be carried out for featureextraction. Then from extracted feature set, feature selection is done by using Kullback-Leibler divergence measure(KLD). We evaluated the proposed approach on document collections BBC that is news article dataset, originating fromBBC News, using classification algorithm, Naive Bayes. Proposed method outperforms in terms of accuracy of textcategorization.
Smita Shedbale, Dr. Kailash Shaw, “N-Gram and KLD Based Efficient Feature Selection Approach for Text Categorization”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 4, Issue 6, pp. 660-668, June 2017.
