The Effectiveness of the Max Entropy Classifier for Feature Selection

Abstract

Feature selection is the task of systematically reducing the number of input features for a classification task. In natural language processing, basic feature selection is often achieved by removing common stop words. In order to more drastically reduce the number of input features, actual feature selection methods such as Mutual Information or Chi-Squared are used on a count-based input representation. We suggest a task-oriented approach to select features based on the weights as learned by a Max Entropy classifier trained on the classification task. The remaining features can then be used by other classifiers to do the actual classification. Experiments on different natural language processing tasks confirm that the weight-based method is comparable to count-based methods. The number of input features can be reduced considerably while maintaining the classification performance.