Combining rough decisions for intelligent text mining using Dempster's rule

Y Bi; SI McClean; TJ Anderson

doi:10.1007/s10462-007-9049-y

Combining rough decisions for intelligent text mining using Dempster's rule

Research output: Contribution to journal › Article › peer-review

Abstract

This paper proposes a novel approach to combining multiple rule sets for intelligent text mining. We develop a boosting-like technique for generating multiple sets of rules based on rough set theory and model decisions inferred from multiple rule sets as evidence for combination by Dempster's rule. This approach builds on our previous work on rough set based methods for mining maximal associations from text collections. Through evaluation on a benchmark data collection, the approach is demonstrated to provide significant improvement over single rule set methods and provides insight into incorporating evidential theory into text mining tasks.

Original language	English
Pages (from-to)	191-209
Journal	Artificial Intelligence Review
Volume	26
Issue number	3
DOIs	https://doi.org/10.1007/s10462-007-9049-y
Publication status	Published (in print/issue) - Oct 2006

Bibliographical note

Other Details
------------------------------------
Journal article awaiting print. This paper proposes a novel approach to combining multiple rule sets for intelligent text mining. We develop a boosting-like technique for generating multiple sets of rules based on rough set theory and model decisions inferred from multiple rule sets as evidence for combination by Dempster's rule. This approach builds on our previous work on rough set based methods for mining maximal associations from text collections. Through evaluation on a benchmark data collection, the approach is demonstrated to provide significant improvement over single rule set methods and provides insight into incorporating evidential theory into text mining tasks.

Keywords

rough decisions
text mining
Dempster's rule

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10.1007/s10462-007-9049-y

http://www.springerlink.com/content/y504768863164015/

Cite this

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title = "Combining rough decisions for intelligent text mining using Dempster's rule",

abstract = "This paper proposes a novel approach to combining multiple rule sets for intelligent text mining. We develop a boosting-like technique for generating multiple sets of rules based on rough set theory and model decisions inferred from multiple rule sets as evidence for combination by Dempster's rule. This approach builds on our previous work on rough set based methods for mining maximal associations from text collections. Through evaluation on a benchmark data collection, the approach is demonstrated to provide significant improvement over single rule set methods and provides insight into incorporating evidential theory into text mining tasks.",

keywords = "rough decisions, text mining, Dempster's rule",

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year = "2006",

month = oct,

doi = "10.1007/s10462-007-9049-y",

language = "English",

volume = "26",

pages = "191--209",

journal = "Artificial Intelligence Review",

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AU - McClean, SI

AU - Anderson, TJ

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PY - 2006/10

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N2 - This paper proposes a novel approach to combining multiple rule sets for intelligent text mining. We develop a boosting-like technique for generating multiple sets of rules based on rough set theory and model decisions inferred from multiple rule sets as evidence for combination by Dempster's rule. This approach builds on our previous work on rough set based methods for mining maximal associations from text collections. Through evaluation on a benchmark data collection, the approach is demonstrated to provide significant improvement over single rule set methods and provides insight into incorporating evidential theory into text mining tasks.

AB - This paper proposes a novel approach to combining multiple rule sets for intelligent text mining. We develop a boosting-like technique for generating multiple sets of rules based on rough set theory and model decisions inferred from multiple rule sets as evidence for combination by Dempster's rule. This approach builds on our previous work on rough set based methods for mining maximal associations from text collections. Through evaluation on a benchmark data collection, the approach is demonstrated to provide significant improvement over single rule set methods and provides insight into incorporating evidential theory into text mining tasks.

KW - rough decisions

KW - text mining

KW - Dempster's rule

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DO - 10.1007/s10462-007-9049-y

M3 - Article

SN - 0269-2821

VL - 26

SP - 191

EP - 209

JO - Artificial Intelligence Review

JF - Artificial Intelligence Review

IS - 3

ER -

Combining rough decisions for intelligent text mining using Dempster's rule

Abstract

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Keywords

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