• INFORMATION THEORETIC METHOD OF FEATURE SELECTION FOR TEXT CATEGORIZATION

Abstract

With the rapid spread of the Internet and the increase in on-line information, the technology for automatically classifying huge amounts of diverse text information has come to play a very important role in today’s world. In the 1990s, the performance of computers improved sharply and it became possible to handle large quantities of text data. This led to the use of the machine learning approach, which is a method of creating classifiers automatically from the text data given in a category label. This approach provides excellent accuracy, reduces labor, and ensures conservative use of resources. In this communication, we discussed that Feature selection plays an important role in Text Categorization (Yiming Yang, Jan O. Pedersen, 1997).We have also deliberated on Automatic feature selection methods such as document frequency thresholding (DF), Information Gain (IG), Mutual Information (MI) and Pointwise Mutual Information (PMI) which are commonly applied in text categorization.