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Volumn 1, Issue , 2005, Pages 319-325

Hybrid intelligent systems: Selecting attributes for soft-computing analysis

Author keywords

Hybrid intelligent systems; Rough sets

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; DATABASE SYSTEMS; ROUGH SET THEORY; SYSTEMS ANALYSIS;

EID: 33751094712     PISSN: 07303157     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/COMPSAC.2005.87     Document Type: Conference Paper
Times cited : (20)

References (12)
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    • Data Mining via Discretization, Generalization and Rough Set Feature Selection
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    • Hu, X.1    Gereone, N.2
  • 8
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    • A. An, N. Gereone, "Rule Quality Measures Improve the Accuracy of Rule Induction: An Experimental Approach Computational Intelligence", Computational Intelligence. August, vol. 17, no. 3, 2001, pp. 409-424.
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    • Selecting Attributes for Soft-Computing Analysis in Hybrid Intelligent Systems
    • P. Pattaraintakorn, N. Gereone, K. Naruedomkul, "Selecting Attributes for Soft-Computing Analysis in Hybrid Intelligent Systems", In: RSFDGrC'05.
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    • Pattaraintakorn, P.1    Gereone, N.2    Naruedomkul, K.3
  • 11
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    • A new rough sets models based on database systems
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  • 12
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    • øhrn, J. Komorowski, A. Skowron, P. Synak., The Design and Implementation of a Knowledge Discovery Toolkit Based on Rough Sets: The Rosetta System., In Rough Sets in Knowledge Discovery, L. Polkowski and A. Skowron (eds.), Physica Verlag, 1998, Software available from http://www.idi.ntnu.no/~aleks/rosetta/.
    • øhrn, J. Komorowski, A. Skowron, P. Synak., "The Design and Implementation of a Knowledge Discovery Toolkit Based on Rough Sets: The Rosetta System.", In Rough Sets in Knowledge Discovery, L. Polkowski and A. Skowron (eds.), Physica Verlag, 1998, Software available from http://www.idi.ntnu.no/~aleks/rosetta/.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.