Using Multiple Sets of Attributes for Text Categorization

Yaxin Bi, Q. Zhang, Shengli Wu, J. Guan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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"
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages2252-2256
Number of pages4
DOIs
Publication statusPublished - 2006
EventProceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, 13-16 August 2006 -
Duration: 1 Jan 2006 → …

Conference

ConferenceProceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, 13-16 August 2006
Period1/01/06 → …

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Rough set theory
Experiments

Cite this

Bi, Y., Zhang, Q., Wu, S., & Guan, J. (2006). Using Multiple Sets of Attributes for Text Categorization. In Unknown Host Publication (pp. 2252-2256) https://doi.org/10.1109/ICMLC.2006.258668
Bi, Yaxin ; Zhang, Q. ; Wu, Shengli ; Guan, J. / Using Multiple Sets of Attributes for Text Categorization. Unknown Host Publication. 2006. pp. 2252-2256
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abstract = "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{"}",
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Bi, Y, Zhang, Q, Wu, S & Guan, J 2006, Using Multiple Sets of Attributes for Text Categorization. in Unknown Host Publication. pp. 2252-2256, Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, 13-16 August 2006, 1/01/06. https://doi.org/10.1109/ICMLC.2006.258668

Using Multiple Sets of Attributes for Text Categorization. / Bi, Yaxin; Zhang, Q.; Wu, Shengli; Guan, J.

Unknown Host Publication. 2006. p. 2252-2256.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - 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"

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