메뉴 건너뛰기




Volumn 2, Issue 3, 2004, Pages 325-348

Is it worth generating rules from neural network ensembles?

Author keywords

Decision trees; Ensembles; Neural networks; Proteins; Rule extraction

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; MATHEMATICAL MODELS; TREES (MATHEMATICS); VECTORS;

EID: 10944228774     PISSN: 15708683     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jal.2004.03.004     Document Type: Article
Times cited : (48)

References (45)
  • 2
    • 0029484103 scopus 로고
    • Survey and critique of techniques for extracting rules from trained artificial neural networks
    • Andrews R. Diederich J. Tickle A.B. Survey and critique of techniques for extracting rules from trained artificial neural networks Knowledge Based Systems 8 6 1995 373-389
    • (1995) Knowledge Based Systems , vol.8 , Issue.6 , pp. 373-389
    • Andrews, R.1    Diederich, J.2    Tickle, A.B.3
  • 3
    • 0036707194 scopus 로고    scopus 로고
    • Rule extraction from local cluster neural nets
    • Andrews R. Geva S. Rule extraction from local cluster neural nets Neurocomput. 47 2002 1-20
    • (2002) Neurocomput. , vol.47 , pp. 1-20
    • Andrews, R.1    Geva, S.2
  • 4
    • 0033957834 scopus 로고    scopus 로고
    • The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000
    • Bairoch A. Apweiler R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000 Nucl. Acids Res. 28 2000 45-48
    • (2000) Nucl. Acids Res. , vol.28 , pp. 45-48
    • Bairoch, A.1    Apweiler, R.2
  • 5
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
    • Bauer E. Kohavi R. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants Machine Learning 36 1999 105-139
    • (1999) Machine Learning , vol.36 , pp. 105-139
    • Bauer, E.1    Kohavi, R.2
  • 6
    • 0001526276 scopus 로고
    • Improving the accuracy of an artificial neural network using multiple differently trained networks
    • Baxt W. Improving the accuracy of an artificial neural network using multiple differently trained networks Neural Comput. 4 1992 772-780
    • (1992) Neural Comput. , vol.4 , pp. 772-780
    • Baxt, W.1
  • 7
    • 0003408496 scopus 로고    scopus 로고
    • UCI repository of machine learning databases
    • University of California, Department of Information and Computer Science, Irvine CA
    • Blake C.L. Merz C.J. UCI repository of machine learning databases http://www.ics.uci.edu/mlearn/MLRepository.html, University of California, Department of Information and Computer Science, Irvine CA
    • Blake, C.L.1    Merz, C.J.2
  • 10
    • 84942937534 scopus 로고    scopus 로고
    • Symbolic rule extraction from the DIMLP neural network
    • S. Wermter, R. Sun (Eds.), Berlin: Springer
    • Bologna G. Symbolic rule extraction from the DIMLP neural network Wermter S. Sun R. Neural Hybrid Systems 2000 240-254 Springer Berlin
    • (2000) Neural Hybrid Systems , pp. 240-254
    • Bologna, G.1
  • 11
    • 2342472859 scopus 로고    scopus 로고
    • A study on rule extraction from several combined neural networks
    • Bologna G. A study on rule extraction from several combined neural networks Internat. J. Neural Syst. 11 3 2001 247-255
    • (2001) Internat. J. Neural Syst. , vol.11 , Issue.3 , pp. 247-255
    • Bologna, G.1
  • 12
    • 0042168792 scopus 로고    scopus 로고
    • A model for single and multiple knowledge based networks
    • Bologna G. A model for single and multiple knowledge based networks Artificial Intelligence in Medicine 28 2003 141-163
    • (2003) Artificial Intelligence in Medicine , vol.28 , pp. 141-163
    • Bologna, G.1
  • 13
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman L. Bagging predictors Machine Learning 26 1996 123-140
    • (1996) Machine Learning , vol.26 , pp. 123-140
    • Breiman, L.1
  • 15
    • 85156234012 scopus 로고    scopus 로고
    • Extracting tree-structured representations of trained networks
    • Craven M. Shavlik J. Extracting tree-structured representations of trained networks Proc. Neural Inform. Process. Syst. 8 1996 24-30
    • (1996) Proc. Neural Inform. Process. Syst. , vol.8 , pp. 24-30
    • Craven, M.1    Shavlik, J.2
  • 16
    • 0003577338 scopus 로고    scopus 로고
    • Extracting comprehensible models from trained neural networks
    • PhD Thesis, University of Wisconsin
    • M. Craven, Extracting comprehensible models from trained neural networks, PhD Thesis, University of Wisconsin, 1996
    • (1996)
    • Craven, M.1
  • 17
    • 0002426982 scopus 로고    scopus 로고
    • Discovery via multiple models
    • Domingos P. Discovery via multiple models Intelligent Data Anal. 2 3 1998 187-202
    • (1998) Intelligent Data Anal. , vol.2 , Issue.3 , pp. 187-202
    • Domingos, P.1
  • 18
    • 0035271419 scopus 로고    scopus 로고
    • A new methodology of extraction, optimization and application of crisp and fuzzy logical rules
    • Duch W. Adamczak R. Grabczewski K. A new methodology of extraction, optimization and application of crisp and fuzzy logical rules Trans. Neural Networks 12 2 2001 277-306
    • (2001) Trans. Neural Networks , vol.12 , Issue.2 , pp. 277-306
    • Duch, W.1    Adamczak, R.2    Grabczewski, K.3
  • 20
    • 0023962833 scopus 로고    scopus 로고
    • Connectionist expert systems
    • Gallant S.I. Connectionist expert systems Comm. ACM 31 1998 152-169
    • (1998) Comm. ACM , vol.31 , pp. 152-169
    • Gallant, S.I.1
  • 21
    • 0035127989 scopus 로고    scopus 로고
    • Symbolic knowledge extraction from trained neural networks: A sound approach
    • Garcez A.S.D. Broda K. Gabbay D.M. Symbolic knowledge extraction from trained neural networks: A sound approach Artificial Intelligence 125 2001 155-207
    • (2001) Artificial Intelligence , vol.125 , pp. 155-207
    • Garcez, A.S.D.1    Broda, K.2    Gabbay, D.M.3
  • 22
    • 0003979924 scopus 로고
    • Introduction to The Theory of Neural Computation
    • Reading, MA: Addison-Wesley
    • Hertz J.A. Krogh A. Palmer R.G. Introduction to The Theory of Neural Computation 1991 Addison-Wesley Reading, MA
    • (1991)
    • Hertz, J.A.1    Krogh, A.2    Palmer, R.G.3
  • 23
    • 0027601884 scopus 로고
    • ANFIS. Adaptive-network-based fuzzy inference system
    • Jang J.S.R. ANFIS. Adaptive-network-based fuzzy inference system Trans. System Man Cybernet. 23 1993 665-685
    • (1993) Trans. System Man Cybernet. , vol.23 , pp. 665-685
    • Jang, J.S.R.1
  • 24
    • 0033280510 scopus 로고    scopus 로고
    • Rule insertion and rule extraction from evolving fuzzy networks: Algorithms and applications for building adaptive intelligent expert systems
    • Kasabov N. Woodford B.J. Rule insertion and rule extraction from evolving fuzzy networks: Algorithms and applications for building adaptive intelligent expert systems Proc. of the International Fuzzy Systems Conference 1999 1406-1411
    • (1999) Proc. of the International Fuzzy Systems Conference , pp. 1406-1411
    • Kasabov, N.1    Woodford, B.J.2
  • 25
    • 0033097962 scopus 로고    scopus 로고
    • A search technique for rule extraction from trained neural networks
    • Krishnan R. Sivakumar G. Bhattacharya P. A search technique for rule extraction from trained neural networks Pattern Recogn. Lett. 20 1999 273-280
    • (1999) Pattern Recogn. Lett. , vol.20 , pp. 273-280
    • Krishnan, R.1    Sivakumar, G.2    Bhattacharya, P.3
  • 26
    • 0036295384 scopus 로고    scopus 로고
    • N-terminal N-myristoylation of proteins: Refinement of the sequence motif and its taxon-specific differences
    • Maurer-Stroh S. Eisenhaber B. Eisenhaber F. N-terminal N-myristoylation of proteins: Refinement of the sequence motif and its taxon-specific differences J. Mol. Biol. 317 2002 523-540
    • (2002) J. Mol. Biol. , vol.317 , pp. 523-540
    • Maurer-Stroh, S.1    Eisenhaber, B.2    Eisenhaber, F.3
  • 27
    • 0026492833 scopus 로고
    • Predicting protein secondary structure using logic-based machine
    • Muggleton S. King R.D. Sternberg M.J.J. Predicting protein secondary structure using logic-based machine Protein Engineering 5 1992 647-657
    • (1992) Protein Engineering , vol.5 , pp. 647-657
    • Muggleton, S.1    King, R.D.2    Sternberg, M.J.J.3
  • 28
    • 0032960792 scopus 로고    scopus 로고
    • Obtaining interpretable fuzzy classification rules from medical data
    • Nauck D. Kruse R. Obtaining interpretable fuzzy classification rules from medical data Artificial Intelligence in Medicine 16 1999 149-169
    • (1999) Artificial Intelligence in Medicine , vol.16 , pp. 149-169
    • Nauck, D.1    Kruse, R.2
  • 29
    • 0000551189 scopus 로고    scopus 로고
    • Popular ensemble methods: An empirical study
    • Opitz D. Maclin R. Popular ensemble methods: An empirical study Artificial Intelligence Res. 11 1999 169-198
    • (1999) Artificial Intelligence Res. , vol.11 , pp. 169-198
    • Opitz, D.1    Maclin, R.2
  • 30
    • 0003500248 scopus 로고
    • C4.5: Programs for Machine Learning
    • Morgan Kaufmann
    • Quinlan J.R. C4.5: Programs for Machine Learning 1993 Morgan Kaufmann
    • (1993)
    • Quinlan, J.R.1
  • 31
    • 0003952786 scopus 로고
    • Principles of Neurodynamics
    • New York: Spartan
    • Rosenblatt F. Principles of Neurodynamics 1962 Spartan New York
    • (1962)
    • Rosenblatt, F.1
  • 33
    • 0342378106 scopus 로고    scopus 로고
    • Neurolinear: From neural networks to oblique decision rules
    • Setiono R. Huan L. Neurolinear: From neural networks to oblique decision rules Neurocomput. 17 1997 1-24
    • (1997) Neurocomput. , vol.17 , pp. 1-24
    • Setiono, R.1    Huan, L.2
  • 34
    • 0034159928 scopus 로고    scopus 로고
    • Generating concise and accurate classification rules for breast cancer diagnosis
    • Setiono R. Generating concise and accurate classification rules for breast cancer diagnosis Artificial Intelligence in Medicine 18 2000 205-219
    • (2000) Artificial Intelligence in Medicine , vol.18 , pp. 205-219
    • Setiono, R.1
  • 36
    • 0032685184 scopus 로고    scopus 로고
    • Symbolic interpretation of artificial neural networks
    • Taha I.A. Ghosh J. Symbolic interpretation of artificial neural networks Trans. Neural Networks 11 3 1999 448-463
    • (1999) Trans. Neural Networks , vol.11 , Issue.3 , pp. 448-463
    • Taha, I.A.1    Ghosh, J.2
  • 37
    • 0003539213 scopus 로고
    • The MONK's problems-a performance comparison of different learning algorithms
    • Technical Report CS-CMU-91-197, Carnegie Mellon University
    • S. Thrun, et al., The MONK's problems-a performance comparison of different learning algorithms, Technical Report CS-CMU-91-197, Carnegie Mellon University, 1991
    • (1991)
    • Thrun, S.1
  • 38
    • 85150197299 scopus 로고
    • Extracting rules from artificial neural networks with distributed representations
    • G. Tesauro, D. S. Touretzky, T. K. Leen (Eds.), Cambridge, MA: MIT Press
    • Thrun S. Extracting rules from artificial neural networks with distributed representations Tesauro G. Touretzky D.S. Leen T.K. Neural Information Processing Systems 7 1995 505-512 MIT Press Cambridge, MA
    • (1995) Neural Information Processing Systems 7 , pp. 505-512
    • Thrun, S.1
  • 39
    • 0004086726 scopus 로고
    • Symbolic knowledge and neural networks: Insertion, refinement and extraction
    • PhD Thesis, University of Wisconsin
    • G. Towell, Symbolic knowledge and neural networks: Insertion, refinement and extraction, PhD Thesis, University of Wisconsin, 1991
    • (1991)
    • Towell, G.1
  • 40
    • 0027678679 scopus 로고
    • The extraction of refined rules from knowledge based neural networks
    • Towell G. Shavlik J. The extraction of refined rules from knowledge based neural networks Machine Learning 131 1993 71-101
    • (1993) Machine Learning , vol.131 , pp. 71-101
    • Towell, G.1    Shavlik, J.2
  • 41
    • 0042669840 scopus 로고    scopus 로고
    • Explaining the output of ensembles in medical decision support on a case by case basis
    • Wall R. Cunningham P. Walsh P. Byrne S. Explaining the output of ensembles in medical decision support on a case by case basis Artificial Intelligence in Medicine 28 2003 141-163
    • (2003) Artificial Intelligence in Medicine , vol.28 , pp. 141-163
    • Wall, R.1    Cunningham, P.2    Walsh, P.3    Byrne, S.4
  • 42
    • 0003529238 scopus 로고
    • Beyond regression: New tools for prediction and analysis in the behavorial sciences
    • PhD Thesis, Harvard University
    • P. Werbos, Beyond regression: New tools for prediction and analysis in the behavioral sciences, PhD Thesis, Harvard University, 1974
    • (1974)
    • Werbos, P.1
  • 45
    • 0038030864 scopus 로고    scopus 로고
    • Extracting symbolic rules from trained neural network ensembles
    • Zhou Z.-H. Jiang Y. Chen S.-F. Extracting symbolic rules from trained neural network ensembles AI Commun. 16 1 2003 3-15
    • (2003) AI Commun. , vol.16 , Issue.1 , pp. 3-15
    • Zhou, Z.-H.1    Jiang, Y.2    Chen, S.-F.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.