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Volumn 29, Issue 1, 2005, Pages 49-64

On acquiring classification knowledge from noisy data based on rough set

Author keywords

Classification; Information granule; Lower approximation; Noisy information system; Randomization analysis; Rough set

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; HEURISTIC METHODS; LEARNING SYSTEMS; MATHEMATICAL MODELS; SET THEORY; SPURIOUS SIGNAL NOISE;

EID: 16244416222     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2005.01.005     Document Type: Article
Times cited : (22)

References (24)
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  • 7
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    • Piasta, Z.1    Lenarcik, A.2
  • 18
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    • On rough set based approaches to induction of decision rules
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    • J. Stefanowski On rough set based approaches to induction of decision rules L. Polkowski A. Skowron Rough sets in data mining and knowledge discovery 1998 Physica-Verlag New York 500 529
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  • 20
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    • On application of rough data mining to automatic construction of student models
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    • F.-H. Wang, and S.-W. Hung On application of rough data mining to automatic construction of student models d. Cheung G.J. Williams Q. Li Fifth Pacific-Asia conference on advances in knowledge discovery and data mining Lecture Notes on Artificial Intelligence 2001 Springer New York 161 166
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  • 21
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  • 24
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    • Ziarko, W. (2002). Set approximation quality measures in the variable precision rough set model. In: Frontiers in AI and Applications (Vol. 87) (pp. 442-452). Amsterdam: Soft Computing Systems, IOS Press. ISSN: 0922-6389.
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    • Ziarko, W.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.