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Volumn 6, Issue 4, 2005, Pages 315-331

Efficient genetic algorithm based data mining using feature selection with Hausdorff distance

Author keywords

Data mining; Evolutionary algorithms; Feature selection; Genetic algorithms; Knowledge discovery; Rule learning

Indexed keywords


EID: 27644539788     PISSN: 1385951X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10799-005-3898-3     Document Type: Article
Times cited : (33)

References (59)
  • 3
    • 0001824610 scopus 로고
    • Adaptive and self-adaptive evolutionary computations
    • M. Palaniswami Y. Attikiouzel R. Marks D. Fogel T. Fukuda (eds.), IEEE Press Piscataway NJ
    • P.J. Angeline 1995 Adaptive and self-adaptive evolutionary computations M. Palaniswami Y. Attikiouzel R. Marks D. Fogel T. Fukuda Computational Intelligence: A Dynamic Systems Perspectives IEEE Press Piscataway, NJ 152-163
    • (1995) Computational Intelligence: A Dynamic Systems Perspectives , pp. 152-163
    • Angeline, P.J.1
  • 4
    • 0042312958 scopus 로고    scopus 로고
    • Two self-adaptive crossover operations for genetic programming
    • P. Angeline K. Kinnear (eds.), MIT Press Cambridge MA
    • P.J. Angeline 1996 Two self-adaptive crossover operations for genetic programming P. Angeline K. Kinnear Advances in Genetic Programming II MIT Press Cambridge, MA 152-163
    • (1996) Advances in Genetic Programming , vol.2 , pp. 152-163
    • Angeline, P.J.1
  • 6
    • 0041800296 scopus 로고
    • Parallel processing mines retail data
    • C. Babcock, Parallel processing mines retail data, Computer World 6 (1994).
    • (1994) Computer World , vol.6
    • Babcock, C.1
  • 9
    • 25444532289 scopus 로고
    • Multistrategy learning from engineering data by integrating inductive generalization and genetic algorithms
    • R. Michalski and G. Tecuci, (eds.) San Francisco: Morgan Kaufmann
    • J. Bala, K. De Jong, and P. Pachowicz, Multistrategy learning from engineering data by integrating inductive generalization and genetic algorithms, in Machine Learning: A Multistartegy Approach Volume IV, R. Michalski and G. Tecuci, (eds.), 1994, San Francisco: Morgan Kaufmann.
    • (1994) Machine Learning: A Multistartegy Approach , vol.4
    • Bala, J.1    De Jong, K.2    Pachowicz, P.3
  • 10
    • 0028468293 scopus 로고
    • Using mutual information for selecting features in supervised neural net learning
    • 10.1109/72.298224 IEEE Transactions on Neural Networks
    • R. Battiti, Using mutual information for selecting features in supervised neural net learning, IEEE Transactions on Neural Networks 5(4) (1994) 537-550. 10.1109/72.298224
    • (1994) , vol.5 , Issue.4 , pp. 537-550
    • Battiti, R.1
  • 16
    • 0009302802 scopus 로고    scopus 로고
    • Exploring self-adaptive methods to improve the efficiency of generating approximate solutions to travelling salesman problems using evolutionary programming
    • P.J. Angeline, R.G. Reynolds, J.R. McDonnell and R. Eberhart, (eds.) Springer
    • K. Chellapilla and D.B. Fogel, Exploring self-adaptive methods to improve the efficiency of generating approximate solutions to travelling salesman problems using evolutionary programming, in P.J. Angeline, R.G. Reynolds, J.R. McDonnell and R. Eberhart, (eds.) Evolutionary Programming VI, Springer, 1997).
    • (1997) Evolutionary Programming , vol.6
    • Chellapilla, K.1    Fogel, D.B.2
  • 18
    • 84942213019 scopus 로고
    • The best two independent measurements are not the two best, IEEE Transactions on Systems, Man, and Cybernetics
    • SMC
    • T.M. Cover, The best two independent measurements are not the two best, IEEE Transactions on Systems, Man, and Cybernetics, SMC-4:1 (1974) 116-117.
    • (1974) , vol.4 , Issue.1 , pp. 116-117
    • Cover, T.M.1
  • 21
    • 0002047785 scopus 로고    scopus 로고
    • Inductive logic programming and knowledge discovery in databases
    • AAAI Press (Menlo Park, Calif., 1996)
    • S. Dzeroski, Inductive logic programming and knowledge discovery in databases, in Advances in Knowledge Discovery and Data Mining, AAAI Press (Menlo Park, Calif., 1996) pp. 117-152.
    • Advances in Knowledge Discovery and Data Mining , pp. 117-152
    • Dzeroski, S.1
  • 22
    • 0014176951 scopus 로고
    • On the choice of variables in classification problems with dichotomous variables
    • 6064031
    • J.D. Elashoff, R.M. Elashoff and G.E. Goldman, On the choice of variables in classification problems with dichotomous variables, Biometrika 54 (1967) 668-670. 6064031
    • (1967) Biometrika , vol.54 , pp. 668-670
    • Elashoff, J.D.1    Elashoff, R.M.2    Goldman, G.E.3
  • 23
    • 0002101112 scopus 로고    scopus 로고
    • A Statistical perspective on knowledge discovery aaai press
    • menlo park, calif., in databases
    • J. Elder and D. Pregibon, A Statistical perspective on knowledge discovery aaai press (menlo park, calif., in databases, in Advances in Knowledge Discovery and Data Mining, 1996).
    • (1996) Advances in Knowledge Discovery and Data Mining
    • Elder, J.1    Pregibon, D.2
  • 25
    • 0033676397 scopus 로고    scopus 로고
    • A multiobjective evolutionary setting for feature selection and a commonality-based crossover operator
    • C. Emmanouilidis, A. Hunter and J. MacIntyre, A multiobjective evolutionary setting for feature selection and a commonality-based crossover operator, in: Proc. of Congress on Evolutionary Computation (2000) 309-316.
    • (2000) Proc. of Congress on Evolutionary Computation , pp. 309-316
    • Emmanouilidis, C.1    Hunter, A.2    MacIntyre, J.3
  • 28
    • 0002432565 scopus 로고
    • Multivariate adaptive regression splines
    • J. Friedman, Multivariate adaptive regression splines, Annals of Statistics 19 (1989, 1992) 1-141.
    • (1989) Annals of Statistics , vol.19 , pp. 1-141
    • Friedman, J.1
  • 38
    • 0016552259 scopus 로고
    • Mathematical methods of feature selection in pattern recognition
    • J. Kittler, Mathematical methods of feature selection in pattern recognition, International Journal of Man-Machine Studies 7 (1975) 609-637.
    • (1975) International Journal of Man-Machine Studies , vol.7 , pp. 609-637
    • Kittler, J.1
  • 40
    • 0031076889 scopus 로고
    • A genetic algorithm based approach to flexible flow-line scheduling with variable lot sizes
    • I. Lee, R. Sikora and M. Shaw, A genetic algorithm based approach to flexible flow-line scheduling with variable lot sizes, IEEE Transactions on Systems, Man, and Cybernetics 27B(1) (1995) 36-54.
    • (1995) IEEE Transactions on Systems, Man, and Cybernetics , vol.27 B , Issue.1 , pp. 36-54
    • Lee, I.1    Sikora, R.2    Shaw, M.3
  • 41
    • 0002097275 scopus 로고
    • Inserting Introns Improves Genetic Algorithm Success Rate: Taking a Cue from Biology
    • R. Belew and L. Booker (eds.)
    • J. Levenick, Inserting Introns Improves Genetic Algorithm Success Rate: Taking a Cue from Biology, in R. Belew and L. Booker (eds.) Proc. of the Fourth Intl. Conf. on Genetic Algorithms, (1991) pp. 123-127.
    • (1991) Proc. of the Fourth Intl. Conf. on Genetic Algorithms , pp. 123-127
    • Levenick, J.1
  • 44
    • 27644579454 scopus 로고    scopus 로고
    • Comparing a genetic algorithm with a rule induction algorithm in the data mining task of dependence modeling
    • E. Noda, A. Freitas and H. Lopes, Comparing a genetic algorithm with a rule induction algorithm in the data mining task of dependence modeling, in: Proc. of the Genetic and Evolutionary Computation Conference (2000) 1080.
    • (2000) Proc. of the Genetic and Evolutionary Computation Conference , pp. 1080
    • Noda, E.1    Freitas, A.2    Lopes, H.3
  • 45
    • 0005235629 scopus 로고    scopus 로고
    • Explicitly defined introns and destructive crossover in genetic programming
    • P. Angeline and K. Kinnear (eds.)
    • P. Nordin, F. Francone and W. Banzhaf, Explicitly defined introns and destructive crossover in genetic programming, in, P. Angeline and K. Kinnear (eds.), Advances in Genetic Programming: Volume 2, (1996), 111-134.
    • (1996) Advances in Genetic Programming , vol.2 , pp. 111-134
    • Nordin, P.1    Francone, F.2    Banzhaf, W.3
  • 52
    • 84947657865 scopus 로고
    • Learning control strategies for a chemical process: A distributed approach
    • R. Sikora, Learning control strategies for a chemical process: A distributed approach, IEEE Expert, (1992) 35-43.
    • (1992) IEEE Expert , pp. 35-43
    • Sikora, R.1
  • 53
    • 0009454988 scopus 로고
    • A double-layered learning approach to acquiring rules for classification: Integrating genetic algorithms with similarity-based learning
    • R. Sikora and M. Shaw, A double-layered learning approach to acquiring rules for classification: Integrating genetic algorithms with similarity-based learning, ORSA Journal on Computing 6(2) (1994) 174-187.
    • (1994) ORSA Journal on Computing , vol.6 , Issue.2 , pp. 174-187
    • Sikora, R.1    Shaw, M.2
  • 55
    • 0001172625 scopus 로고
    • Note on optimal selection of independent binary-valued features for pattern recognition
    • G.T. Toussaint, Note on optimal selection of independent binary-valued features for pattern recognition, IEEE Transactions on Information Theory IT-17 (1971), 618.
    • (1971) IEEE Transactions on Information Theory IT-17 , pp. 618
    • Toussaint, G.T.1
  • 56
    • 0042739598 scopus 로고    scopus 로고
    • How to shift bias: Lessons from the baldwin effect
    • P. Turney, How to shift bias: Lessons from the baldwin effect, Evolutionary Computation 4(3) (1997) 271-295.
    • (1997) Evolutionary Computation , vol.4 , Issue.3 , pp. 271-295
    • Turney, P.1
  • 57
    • 0039271793 scopus 로고
    • Improving a rule induction system using genetic algorithms
    • R. Michalski and G. Tecuci (eds.) (San Francisco: Morgan Kaufmann)
    • H. Vafaie and K. De Jong, Improving a rule induction system using genetic algorithms, in R. Michalski and G. Tecuci (eds.), Machine Learning: A Multistartegy Approach Volume IV, (San Francisco: Morgan Kaufmann 1994).
    • (1994) Machine Learning: A Multistartegy Approach , vol.4
    • Vafaie, H.1    De Jong, K.2
  • 59
    • 0032028297 scopus 로고    scopus 로고
    • Feature subset selection using a genetic algorithm
    • 10.1109/5254.671091
    • J. Yang and V. Honavar, Feature subset selection using a genetic algorithm, IEEE Intelligent Systems 13(2) (1998) 44-49. 10.1109/ 5254.671091
    • (1998) IEEE Intelligent Systems , vol.13 , Issue.2 , pp. 44-49
    • Yang, J.1    Honavar, V.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.