Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Automated learning of decision rules for text categorization
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Advances in the Dempster-Shafer theory of evidence
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Interpretations of belief functions in the theory of rough sets
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Artificial Intelligence
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Communications of the ACM
BoosTexter: A Boosting-based Systemfor Text Categorization
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Information Retrieval
Rough Sets: Theoretical Aspects of Reasoning about Data
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ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Discretizing continuous attributes in AdaBoost for text categorization
ECIR'03 Proceedings of the 25th European conference on IR research
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A neural network classifier based on Dempster-Shafer theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Soft fuzzy rough sets and its application in decision making
Artificial Intelligence Review
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An important issue in text mining is how to make use of multiple pieces knowledge discovered to improve future decisions. In this paper, we propose a new approach to combining multiple sets of rules for text categorization using Dempster's rule of combination. We develop a boosting-like technique for generating multiple sets of rules based on rough set theory and model classification decisions from multiple sets of rules as pieces of evidence which can be combined by Dempster's rule of combination. We apply these methods to 10 of the 20-newsgroups--a benchmark data collection (Baker and McCallum 1998), individually and in combination. Our experimental results show that the performance of the best combination of the multiple sets of rules on the 10 groups of the benchmark data is statistically significant and better than that of the best single set of rules. The comparative analysis between the Dempster---Shafer and the majority voting (MV) methods along with an overfitting study confirm the advantage and the robustness of our approach.