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Volumn 229, Issue , 2013, Pages 106-121

Unsupervised fuzzy-rough set-based dimensionality reduction

Author keywords

Attribute reduction; Fuzzy set; Rough set; Unsupervised feature selection; Unsupervised learning

Indexed keywords

ATTRIBUTE REDUCTION; DATA OBJECTS; DATA SETS; DECISION CLASS; DIMENSIONALITY REDUCTION; DISCRETISATION; DOMAIN EXPERTS; FEATURE SELECTION ALGORITHM; FEATURE VECTORS; FUZZY-ROUGH SETS; GENE EXPRESSION ANALYSIS; PREDICTIVE ACCURACY; REAL-VALUED DATA; REAL-WORLD APPLICATION; ROUGH SET; SUPERSETS; TEXT CLASSIFICATION; THRESHOLDING; UNSUPERVISED FEATURE SELECTION;

EID: 84873284210     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2012.12.001     Document Type: Article
Times cited : (73)

References (40)
  • 3
    • 0033683275 scopus 로고    scopus 로고
    • Maximum entropy and maximum likelihood criteria for feature selection from multi-variate data
    • S. Basu, C.A. Micchelli, P. Olsen, Maximum entropy and maximum likelihood criteria for feature selection from multi-variate data, in: Proc. IEEE Intl. Symp. Circuits and Systems, 2000, pp. 267-270.
    • (2000) Proc. IEEE Intl. Symp. Circuits and Systems , pp. 267-270
    • Basu, S.1    Micchelli, C.A.2    Olsen, P.3
  • 6
    • 0015127383 scopus 로고
    • Feature selection with a linear dependence measure
    • S.K. Das Feature selection with a linear dependence measure IEEE Transactions on Computers 20 9 1971 1106 1109
    • (1971) IEEE Transactions on Computers , vol.20 , Issue.9 , pp. 1106-1109
    • Das, S.K.1
  • 11
    • 0002878444 scopus 로고    scopus 로고
    • Feature subset selection and order identification for unsupervised learning
    • P. Langley, Morgan Kaufmann Publishers San Francisco, CA
    • J.G. Dy, and C.E. Brodley Feature subset selection and order identification for unsupervised learning P. Langley, Proceedings of the Seventeenth international Conference on Machine Le arning 2000 Morgan Kaufmann Publishers San Francisco, CA 247 254
    • (2000) Proceedings of the Seventeenth International Conference on Machine le Arning , pp. 247-254
    • Dy, J.G.1    Brodley, C.E.2
  • 13
    • 0004060921 scopus 로고    scopus 로고
    • Ph.D. Thesis, Department of Computer Science, University of Waikato, Hamilton, New Zealand
    • M.A. Hall, Correlation-based feature selection machine learning, Ph.D. Thesis, Department of Computer Science, University of Waikato, Hamilton, New Zealand, 1998.
    • (1998) Correlation-based Feature Selection Machine Learning
    • Hall, M.A.1
  • 17
    • 10944249572 scopus 로고    scopus 로고
    • Semantics-preserving dimensionality reduction: Rough and fuzzy-rough-based approaches
    • DOI 10.1109/TKDE.2004.96
    • R. Jensen, and Q. Shen Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches IEEE Transactions on Knowledge and Data Engineering 16 12 2004 1457 1471 (Pubitemid 40010921)
    • (2004) IEEE Transactions on Knowledge and Data Engineering , vol.16 , Issue.12 , pp. 1457-1471
    • Jensen, R.1    Shen, Q.2
  • 18
    • 68849126540 scopus 로고    scopus 로고
    • New approaches to fuzzy-rough feature selection
    • R. Jensen, and Q. Shen New approaches to fuzzy-rough feature selection IEEE Transactions on Fuzzy Systems 17 4 2009 824 838
    • (2009) IEEE Transactions on Fuzzy Systems , vol.17 , Issue.4 , pp. 824-838
    • Jensen, R.1    Shen, Q.2
  • 19
    • 24344503628 scopus 로고    scopus 로고
    • Evolutionary model selection in unsupervised learning
    • Y. Kim, W.N. Street, and F. Menczer Evolutionary model selection in unsupervised learning Intelligent Data Analysis 6 6 2002 531 556
    • (2002) Intelligent Data Analysis , vol.6 , Issue.6 , pp. 531-556
    • Kim, Y.1    Street, W.N.2    Menczer, F.3
  • 21
    • 68749092107 scopus 로고    scopus 로고
    • Unsupervised feature selection and general pattern discovery using Self-Organizing Maps for gaining insights into the nature of seismic wavefields
    • A. Köhler, M. Ohrnberger, and F. Scherbaum Unsupervised feature selection and general pattern discovery using Self-Organizing Maps for gaining insights into the nature of seismic wavefields Computer Geoscience 35 9 2009 1757 1767
    • (2009) Computer Geoscience , vol.35 , Issue.9 , pp. 1757-1767
    • Köhler, A.1    Ohrnberger, M.2    Scherbaum, F.3
  • 22
    • 0000012317 scopus 로고    scopus 로고
    • Toward optimal feature selection
    • D. Koller, M. Sahami, Toward optimal feature selection, in: Proceeding of ICML, 1996, pp. 284-292.
    • (1996) Proceeding of ICML , pp. 284-292
    • Koller, D.1    Sahami, M.2
  • 28
    • 84945942894 scopus 로고    scopus 로고
    • Unsupervised feature selection using multi-objective genetic algorithms for handwritten word recognition
    • August 03-06, 2003 ICDAR. IEEE Computer Society Washington, DC
    • M. Morita, R. Sabourin, F. Bortolozzi, and C.Y. Suen Unsupervised feature selection using multi-objective genetic algorithms for handwritten word recognition Proc. of the Seventh international Conference on Document Analysis and Recognition vol. 22(August 03-06, 2003) 2003 ICDAR. IEEE Computer Society Washington, DC
    • (2003) Proc. of the Seventh International Conference on Document Analysis and Recognition , vol.22
    • Morita, M.1    Sabourin, R.2    Bortolozzi, F.3    Suen, C.Y.4
  • 33
    • 24344458137 scopus 로고    scopus 로고
    • Feature selection based on mutual information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy
    • DOI 10.1109/TPAMI.2005.159
    • H.C. Peng, F. Long, and C. Ding Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy IEEE Transactions on Pattern Analysis and Machine Intelligence 27 8 2005 1226 1238 (Pubitemid 41245053)
    • (2005) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.27 , Issue.8 , pp. 1226-1238
    • Peng, H.1    Long, F.2    Ding, C.3
  • 34
    • 0003500248 scopus 로고
    • The Morgan Kaufmann Series in Machine Learning Morgan Kaufmann Publishers San Mateo, CA
    • J.R. Quinlan C4.5: Programs for Machine Learning The Morgan Kaufmann Series in Machine Learning 1993 Morgan Kaufmann Publishers San Mateo, CA
    • (1993) C4.5: Programs for Machine Learning
    • Quinlan, J.R.1
  • 35
    • 0036498107 scopus 로고    scopus 로고
    • A comparative study of fuzzy rough sets
    • DOI 10.1016/S0165-0114(01)00032-X, PII S016501140100032X
    • A.M. Radzikowska, and E.E. Kerre A comparative study of fuzzy rough sets Fuzzy Sets and Systems 126 2 2002 137 155 (Pubitemid 34116140)
    • (2002) Fuzzy Sets and Systems , vol.126 , Issue.2 , pp. 137-155
    • Radzikowska, A.M.1    Kerre, E.E.2
  • 36
    • 2442528339 scopus 로고    scopus 로고
    • Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring
    • DOI 10.1016/j.patcog.2003.10.016, PII S0031320303004242
    • Q. Shen, and R. Jensen Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring Pattern Recognition 37 7 2004 1351 1363 (Pubitemid 38653366)
    • (2004) Pattern Recognition , vol.37 , Issue.7 , pp. 1351-1363
    • Shen, Q.1    Jensen, R.2
  • 40
    • 0032142371 scopus 로고    scopus 로고
    • A comparative study of fuzzy sets and rough sets
    • Y. Yao A comparative study of fuzzy sets and rough sets Information Sciences 109 1998 21 47
    • (1998) Information Sciences , vol.109 , pp. 21-47
    • Yao, Y.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.