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Using Multiple Sets of Attributes for Text Categorization

Bi, Yaxin, Zhang, Q., Wu, Shengli and Guan, J. (2006) Using Multiple Sets of Attributes for Text Categorization. In: Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, 13-16 August 2006. IEEE Press. 4 pp. [Conference contribution]

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DOI: 10.1109/ICMLC.2006.258668


This paper investigates how multiple sets of attributes can be generated using a rough sets-based inductive learning method and how they can be combined for improving classification decisions, particularly in the context of text categorization, by using Dempster's rule of combination. We first propose a boosting-like technique for generating multiple sets of attributes based on rough set theory, and a method for transforming multiple sets of attributes to multiple sets of rules, and then model classification decisions inferred by the rules as pieces of evidence. The various experiments have been carried out on 10 out of the 20-newsgroups - a benchmark data collection ndividually and in combination. Our experimental results support the claim that "decisions made by multiple experts would be more effective than any one if their individual judgments are appropriately combined"

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:25636
Deposited By: Dr Yaxin Bi
Deposited On:11 Dec 2013 10:46
Last Modified:11 Dec 2013 10:46

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