메뉴 건너뛰기




Volumn 3, Issue , 2005, Pages 2506-2513

Balanced accuracy for feature subset selection with genetic algorithms

Author keywords

[No Author keywords available]

Indexed keywords

BEST FITNESS FUNCTION; FEATURE SELECTION ALGORITHMS; NAIVE BYES CLASSIFIER; WRAPPERS;

EID: 27144489689     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (2)

References (22)
  • 1
    • 0000751098 scopus 로고    scopus 로고
    • Using learning to facilitate the evolution of features for recognizing visual concepts
    • J. Bala, K. D. Jong, J. Huang, H. Vafaie, and H. Wechsler. Using learning to facilitate the evolution of features for recognizing visual concepts. Evolutionary Computation, 4:297-311, 1996.
    • (1996) Evolutionary Computation , vol.4 , pp. 297-311
    • Bala, J.1    Jong, K.D.2    Huang, J.3    Vafaie, H.4    Wechsler, H.5
  • 2
    • 0001185873 scopus 로고    scopus 로고
    • An essay towards solving a problem in the doctrine of chances
    • T. Bayes. An essay towards solving a problem in the doctrine of chances. Phil. Trans. Roy. Soc., 53, 1763.
    • Phil. Trans. Roy. Soc. , vol.53 , pp. 1763
    • Bayes, T.1
  • 3
    • 0003408496 scopus 로고    scopus 로고
    • University of California, Irvine, Dept. of Information and Computer Sciences
    • C. L. Blake and C. J. Merz. UCI repository of machine learning databases. University of California, Irvine, Dept. of Information and Computer Sciences, 1998. http://www.ics.uci.edu/~mlearn/MLRepository.html.
    • (1998) UCI Repository of Machine Learning Databases
    • Blake, C.L.1    Merz, C.J.2
  • 4
    • 0347020326 scopus 로고
    • Dynamic feature set training of neural nets for classification
    • J. R. McDonnell, R. G. Reynolds, and D. B. Fogel, editors. MIT Press, Cambridge, MA
    • T. W. Brotherton and P. K. Simpson. Dynamic feature set training of neural nets for classification. In J. R. McDonnell, R. G. Reynolds, and D. B. Fogel, editors, Evolutionary Programming IV, pages 83-94. MIT Press, Cambridge, MA, 1995.
    • (1995) Evolutionary Programming IV , pp. 83-94
    • Brotherton, T.W.1    Simpson, P.K.2
  • 5
    • 27144519762 scopus 로고    scopus 로고
    • Feature subset selection, class separability and genetic algorithms
    • Springer-Verlag
    • E. Cantú-Paz. Feature subset selection, class separability and genetic algorithms. In GECCO-04, LNCS 3102, pages 959-970. Springer-Verlag, 2004.
    • (2004) GECCO-04, LNCS , vol.3102 , pp. 959-970
    • Cantú-Paz, E.1
  • 7
    • 0033185794 scopus 로고    scopus 로고
    • The gambler's ruin problem, genetic algorithms, and the sizing of populations
    • G. Harik, E. Cantú-Paz, D. E. Goldberg, and B. L. Miller. The gambler's ruin problem, genetic algorithms, and the sizing of populations. Evolutionary Computation, 7:231-253, 1999.
    • (1999) Evolutionary Computation , vol.7 , pp. 231-253
    • Harik, G.1    Cantú-Paz, E.2    Goldberg, D.E.3    Miller, B.L.4
  • 8
    • 17744402661 scopus 로고    scopus 로고
    • Feature subset selection by bayesian networks based optimization
    • I. Inza, P. L. naga, R. Etxeberria, and B. Sierra. Feature subset selection by bayesian networks based optimization. Artificial Intelligence, 123:157-184, 199.
    • Artificial Intelligence , vol.123 , pp. 157-184
    • Inza, I.1    Naga, P.L.2    Etxeberria, R.3    Sierra, B.4
  • 9
    • 63249112814 scopus 로고
    • Dimensionality and sample size considerations in pattern recognition in practice
    • P. R. Krishnaiah and L. N. Kanal, editors. North-Holland
    • A. K. Jain and B. Chandrasekaran. Dimensionality and sample size considerations in pattern recognition in practice. In P. R. Krishnaiah and L. N. Kanal, editors, Handbook of Statistics, volume 2, pages 835-855. North-Holland, 1982.
    • (1982) Handbook of Statistics , vol.2 , pp. 835-855
    • Jain, A.K.1    Chandrasekaran, B.2
  • 15
    • 27144507183 scopus 로고    scopus 로고
    • Multi-objective algorithms for attribute subset selection in data mining
    • C. A. C. Coello and G. B. Lamont, editors, Applications of Multi-Objective Evolutionary Algorithms, chapter 25. World Scientific
    • G. L. Pappa, A. A. Freitas, and C. A. A. Kaestner. Multi-objective algorithms for attribute subset selection in data mining. In C. A. C. Coello and G. B. Lamont, editors, Applications of Multi-Objective Evolutionary Algorithms, volume 1 of Advances in Natural Computation, chapter 25, pages 603-626. World Scientific, 2004.
    • (2004) Advances in Natural Computation , vol.1 , pp. 603-626
    • Pappa, G.L.1    Freitas, A.A.2    Kaestner, C.A.A.3
  • 16
    • 27144541118 scopus 로고    scopus 로고
    • Gafacilitated knowledge discovery and pattern recognition optimization applied to the biochemistry of protein solvation
    • M. R. Peterson, T. E. Doom, and M. L. Raymer. Gafacilitated knowledge discovery and pattern recognition optimization applied to the biochemistry of protein solvation. In GECCO 2004 Proceedings, LNCS 3102, pages 426-437, 2004.
    • (2004) GECCO 2004 Proceedings, LNCS , vol.3102 , pp. 426-437
    • Peterson, M.R.1    Doom, T.E.2    Raymer, M.L.3
  • 18
    • 0142009655 scopus 로고    scopus 로고
    • Knowledge discovery in medical and biological datasets using a hybrid bayes classifier/evolutionary algorithm
    • M. L. Raymer, T. E. Doom, L. A. Kuhn, and W. F. Punch. Knowledge discovery in medical and biological datasets using a hybrid bayes classifier/evolutionary algorithm. IEEE Transactions on Evolutionary Computation, 33(5):802-813, 2003.
    • (2003) IEEE Transactions on Evolutionary Computation , vol.33 , Issue.5 , pp. 802-813
    • Raymer, M.L.1    Doom, T.E.2    Kuhn, L.A.3    Punch, W.F.4
  • 19
    • 0024895461 scopus 로고
    • A note on genetic algorithms for large-scale feature selection
    • W. Siedlecki and J. Sklansky. A note on genetic algorithms for large-scale feature selection. Pattern Recognition Letters, 10:335-347, 1989.
    • (1989) Pattern Recognition Letters , vol.10 , pp. 335-347
    • Siedlecki, W.1    Sklansky, J.2
  • 21
    • 0015125457 scopus 로고
    • A direct method of nonparametric measurement selection
    • A. Whitney. A direct method of nonparametric measurement selection. IEEE Transactions on Computers, 20:1100-1103, 1971.
    • (1971) IEEE Transactions on Computers , vol.20 , pp. 1100-1103
    • Whitney, A.1
  • 22
    • 0000961364 scopus 로고    scopus 로고
    • Feature subset selection using a genetic algorithm
    • H. Liu and H. Motoda, editors, chapter 8. Kluwer Academic Publishers, Norwell, MA
    • J. Yang and V. Honavar. Feature subset selection using a genetic algorithm. In H. Liu and H. Motoda, editors, Feature Extraction, Construction and Selection: A Data Mining Perspective, chapter 8, pages 117-136. Kluwer Academic Publishers, Norwell, MA, 1998.
    • (1998) Feature Extraction, Construction and Selection: A Data Mining Perspective , pp. 117-136
    • Yang, J.1    Honavar, V.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.