Two-stage text feature selection method using fuzzy entropy measure and an t colony optimization

Text categorization is widely used when organizing documents in a digital form. Due to the increasing number of documents in digital form, automated text categorization has been emerged as an appropriate tool to classify documents into predefined categories. High dimensionality of the feature space is a common problem in text categorization. Most of the features affecting the classifier performance are irrelevant and redundant. Hence, feature selection is used to reduce feature space thus increasing classifier performance. In this paper, a two-stage method is proposed for text feature selection. At the first stage a filtering technique using the fuzzy entropy measure is applied and features are ranked based on their values. Then, features with the values higher than a threshold are removed from feature set. In the second stage, an ant colony optimization approach is employed to select features from the reduced feature space in the first stage. The proposed method is evaluated through the use of the k-nearest neighbor classifier on top 10 Retures-21578 categories. The experimental results obtained, show the efficiency of the proposed method.