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Volumn 147, Issue 1, 2004, Pages 39-56

Learning hybrid neuro-fuzzy classifier models from data: To combine or not to combine?

Author keywords

Classifier combination; Cross validation; Ensembles of classifiers; Neuro fuzzy classifier; Pattern recognition; Resampling techniques

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; DATABASE SYSTEMS; ERROR DETECTION; HYBRID COMPUTERS; LEARNING SYSTEMS; LOGIC DESIGN; NEURAL NETWORKS; PARAMETER ESTIMATION; PATTERN RECOGNITION;

EID: 4344642807     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fss.2003.11.010     Document Type: Conference Paper
Times cited : (34)

References (21)
  • 1
    • 0029242750 scopus 로고
    • A method for fuzzy rules extraction directly from numerical data and its application to pattern classification
    • Abe S., Lang M. A method for fuzzy rules extraction directly from numerical data and its application to pattern classification. IEEE Trans. Fuzzy Systems. 3(1):1995;18-28.
    • (1995) IEEE Trans. Fuzzy Systems , vol.3 , Issue.1 , pp. 18-28
    • Abe, S.1    Lang, M.2
  • 2
    • 0032591561 scopus 로고    scopus 로고
    • Fuzzy models and potential outliers
    • IEEE Press, New York
    • M. Berthold, Fuzzy models and potential outliers, Proc. NAFIPS-99, IEEE Press, New York, 1999, pp. 532-535.
    • (1999) Proc. NAFIPS-99 , pp. 532-535
    • Berthold, M.1
  • 5
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman L. Bagging predictors. Mach. Learn. 24(2):1996;123-140.
    • (1996) Mach. Learn. , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 6
    • 0031361611 scopus 로고    scopus 로고
    • Machine learning research: Four current directions
    • Dietterich T.G. Machine learning research. four current directions AI Mag. 18(4):1997;97-136.
    • (1997) AI Mag. , vol.18 , Issue.4 , pp. 97-136
    • Dietterich, T.G.1
  • 7
    • 0034250160 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
    • Dietterich T.G. An experimental comparison of three methods for constructing ensembles of decision trees. bagging, boosting, and randomization Mach. Learn. 40:2000;139-157.
    • (2000) Mach. Learn. , vol.40 , pp. 139-157
    • Dietterich, T.G.1
  • 8
    • 0010360453 scopus 로고    scopus 로고
    • Data editing for neuro-fuzzy classifiers
    • Abstract page 77, Paper no. #1824-036, Paisley, Scotland, June, ISBN: 3-906454-27-4
    • B. Gabrys, Data editing for neuro-fuzzy classifiers, Proc. SOCO'2001 Conf., Abstract page 77, Paper no. #1824-036, Paisley, Scotland, June 2001, ISBN: 3-906454-27-4.
    • (2001) Proc. SOCO'2001 Conf.
    • Gabrys, B.1
  • 9
  • 10
    • 0036732448 scopus 로고    scopus 로고
    • Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems
    • Gabrys B. Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems. Internat. J. Approx. Reason. 30(3):2002;149-179.
    • (2002) Internat. J. Approx. Reason. , vol.30 , Issue.3 , pp. 149-179
    • Gabrys, B.1
  • 11
    • 0036689988 scopus 로고    scopus 로고
    • Agglomerative learning algorithms for general fuzzy min-max neural network
    • Gabrys B. Agglomerative learning algorithms for general fuzzy min-max neural network. J. VLSI Signal Process. Systems. 32(1-2):2002;67-82.
    • (2002) J. VLSI Signal Process. Systems , vol.32 , Issue.1-2 , pp. 67-82
    • Gabrys, B.1
  • 12
    • 0033197896 scopus 로고    scopus 로고
    • Neural networks based decision support in presence of uncertainties
    • Gabrys B., Bargiela A. Neural networks based decision support in presence of uncertainties. J. Water Resources Plann. Manage. 125(5):1999;272-280.
    • (1999) J. Water Resources Plann. Manage. , vol.125 , Issue.5 , pp. 272-280
    • Gabrys, B.1    Bargiela, A.2
  • 13
    • 0034187078 scopus 로고    scopus 로고
    • General fuzzy min-max neural network for clustering and classification
    • Gabrys B., Bargiela A. General fuzzy min-max neural network for clustering and classification. IEEE Trans. Neural Networks. 11(3):2000;769-783.
    • (2000) IEEE Trans. Neural Networks , vol.11 , Issue.3 , pp. 769-783
    • Gabrys, B.1    Bargiela, A.2
  • 15
    • 0029487318 scopus 로고
    • Building precise classifiers with automatic rule extraction
    • Perth, Australia
    • K.-P. Huber, M. Berthold, Building precise classifiers with automatic rule extraction, Proc. IEEE Internat. Conf. Neural Networks (IJCNN), Vol. 3, Perth, Australia, 1995, pp. 1263-1268.
    • (1995) Proc. IEEE Internat. Conf. Neural Networks (IJCNN) , vol.3 , pp. 1263-1268
    • Huber, K.-P.1    Berthold, M.2
  • 17
    • 0001703957 scopus 로고    scopus 로고
    • A neuro-fuzzy method to learn fuzzy classification rules from data
    • Nauck D., Kruse R. A neuro-fuzzy method to learn fuzzy classification rules from data. Fuzzy Sets and Systems. 89(3):1997;277-288.
    • (1997) Fuzzy Sets and Systems , vol.89 , Issue.3 , pp. 277-288
    • Nauck, D.1    Kruse, R.2
  • 20
    • 0005946314 scopus 로고    scopus 로고
    • An overview of classifier fusion methods
    • Prof. M. Crowe (Ed.), University of Paisley
    • D. Ruta, B. Gabrys, An overview of classifier fusion methods, in: Prof. M. Crowe (Ed.), Computing and Information Systems, University of Paisley, Vol. 7(1), 2000, pp. 1-10.
    • (2000) Computing and Information Systems , vol.7 , Issue.1 , pp. 1-10
    • Ruta, D.1    Gabrys, B.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.