Strong Rules Learning Algorithm for Ensemble Text Classification

Currently, most text classifiers apply machine learning methods, while ignore traditional methods based on classification rules. In this paper, we propose a strong covering algorithm (called SCA) for generating strong classification rules and view the rules-based classifier as a component classifier in the ensemble text classifier. SCA extracts noun phrase to index document based-on our proposed Exhaustive Noun-Phrase Extraction Algorithm. Experimental results show that the ensemble classifier integrating the strong rules achieves an approximately 8% improvement as compared to bi-gram classifier and 15% improvement as compared to the single rule-based classifier.