Feature Selection Using Improved Mutual Information for Text Classification

A major characteristic of text document classification problem is extremely high dimensionality of text data. In this paper we present two algorithms for feature (word) selection for the purpose of text classification. We used sequential forward selection methods based on improved mutual information introduced by Battiti [1] and Kwak and Choi [6] for non-textual data. These feature evaluation functions take into consideration how features work together. The performance of these evaluation functions compared to the information gain which evaluate features individually is discussed. We present experimental results using naive Bayes classifier based on multinomial model on the Reuters data set. Finally, we analyze the experimental results from various perspectives, including F1-measure, precision and recall. Preliminary experimental results indicate the effectiveness of the proposed feature selection algorithms in a text classification problem.