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Volumn 45, Issue 10, 2012, Pages 3676-3686

Application of global optimization methods to model and feature selection

Author keywords

Cross Entropy Method; Feature selection; Hyper parameters optimization; Particle swarm optimization; Support vector machines

Indexed keywords

CROSS-ENTROPY METHOD; DATA MINING APPLICATIONS; EXHAUSTIVE SEARCH; FEATURE SUBSET; FINITE SUBSETS; GENERALIZATION PERFORMANCE; GLOBAL OPTIMIZATION METHOD; INDUCTION ALGORITHMS; LARGE DATASETS; OPTIMIZATION APPROACH; OPTIMIZATION PROBLEMS; OPTIMIZATION PROCEDURES; PARAMETER VALUES; PREDICTION MODEL;

EID: 84861820472     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2012.04.015     Document Type: Article
Times cited : (46)

References (49)
  • 1
    • 79551472970 scopus 로고    scopus 로고
    • The cross-entropy method in multi-objective optimisation: An assessment
    • J. Bekker, and C. Aldrich The cross-entropy method in multi-objective optimisation: an assessment European Journal of Operational Research 211 May (1) 2011 112 121
    • (2011) European Journal of Operational Research , vol.211 , Issue.MAY 1 , pp. 112-121
    • Bekker, J.1    Aldrich, C.2
  • 2
    • 0031334221 scopus 로고    scopus 로고
    • Selection of relevant features and examples in machine learning
    • A. Blum, and P. Langley Selection of relevant features and examples in machine learning Artificial Intelligence 97 1997 245 271
    • (1997) Artificial Intelligence , vol.97 , pp. 245-271
    • Blum, A.1    Langley, P.2
  • 5
    • 17444366936 scopus 로고    scopus 로고
    • Solving the vehicle routing problem with stochastic demands using the cross entropy method
    • K. Chepuri, and T.H. de Mello Solving the vehicle routing problem with stochastic demands using the cross entropy method Annals of Operations Research 134 2005 153 181
    • (2005) Annals of Operations Research , vol.134 , pp. 153-181
    • Chepuri, K.1    De Mello, T.H.2
  • 7
    • 0036464756 scopus 로고    scopus 로고
    • The particle swarm-explosion, stability, and convergence in a multidimensional complex space
    • M. Clerc, and J. Kennedy The particle swarm-explosion, stability, and convergence in a multidimensional complex space IEEE Transaction on Evolutionary Computation 6 1 2002 58 73
    • (2002) IEEE Transaction on Evolutionary Computation , vol.6 , Issue.1 , pp. 58-73
    • Clerc, M.1    Kennedy, J.2
  • 9
    • 34249753618 scopus 로고
    • Support-vector networks
    • C. Cortes, V. Vapnik, Support-vector networks, Machine Learning 20 (3) (1995) 273-297, http://dx.doi.org/10.1023/A:1022627411411.
    • (1995) Machine Learning , vol.20 , Issue.3 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 10
    • 84926662675 scopus 로고
    • Nearest neighbor pattern classification
    • T. Cover, P. Hart, Nearest neighbor pattern classification, IEEE Transactions on Information Theory, 13 (1) (1967) 21-27, http://dx.doi.org/10. 1109/TIT.1967.1053964
    • (1967) IEEE Transactions on Information Theory , vol.13 , Issue.1 , pp. 21-27
    • Cover, T.1    Hart, P.2
  • 12
    • 58149219602 scopus 로고    scopus 로고
    • Genetic algorithm for feature subset selection with exploitation of feature correlations from continuous wavelet transform: A real-case application
    • A. Okatan, International Computational Intelligence Society
    • G.V. Dijck, M.M.V. Hulle, and M. Wevers Genetic algorithm for feature subset selection with exploitation of feature correlations from continuous wavelet transform: a real-case application A. Okatan, International Conference on Computational Intelligence 2004 International Computational Intelligence Society 34 38
    • (2004) International Conference on Computational Intelligence , pp. 34-38
    • Dijck, G.V.1    Hulle, M.M.V.2    Wevers, M.3
  • 20
    • 0037312471 scopus 로고    scopus 로고
    • A note on the universal approximation capability of support vector machines
    • B. Hammer, and K. Gersmann A note on the universal approximation capability of support vector machines Neural Processing Letters 17 1 2003 43 53
    • (2003) Neural Processing Letters , vol.17 , Issue.1 , pp. 43-53
    • Hammer, B.1    Gersmann, K.2
  • 21
    • 0037822222 scopus 로고    scopus 로고
    • Asymptotic behaviors of support vector machines with Gaussian kernel
    • S.S. Keerthi, and C.-J. Lin Asymptotic behaviors of support vector machines with Gaussian kernel Neural Computation 15 2003 1667 1689
    • (2003) Neural Computation , vol.15 , pp. 1667-1689
    • Keerthi, S.S.1    Lin, C.-J.2
  • 23
    • 0027002164 scopus 로고
    • The feature selection problem: Traditional methods and a new algorithm
    • K. Kira, L.A. Rendell, The feature selection problem: traditional methods and a new algorithm, in: Proceedings of AAAI-92, 1992, pp. 129-134.
    • (1992) Proceedings of AAAI-92 , pp. 129-134
    • Kira, K.1    Rendell, L.A.2
  • 24
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • R. Kohavi, and G.H. John Wrappers for feature subset selection Artificial Intelligence 97 1997 273 324
    • (1997) Artificial Intelligence , vol.97 , pp. 273-324
    • Kohavi, R.1    John, G.H.2
  • 25
    • 0000012317 scopus 로고    scopus 로고
    • Toward optimal feature selection
    • D. Koller, M. Sahami, Toward optimal feature selection, in: ICML, 1996, pp. 284-292.
    • (1996) ICML , pp. 284-292
    • Koller, D.1    Sahami, M.2
  • 26
    • 84992726552 scopus 로고
    • Estimating attributes: Analysis and extensions of relief
    • I. Kononenko, Estimating attributes: analysis and extensions of relief, in: European Conference on Machine Learning, 1994, pp. 171-182.
    • (1994) European Conference on Machine Learning , pp. 171-182
    • Kononenko, I.1
  • 28
    • 3843050541 scopus 로고    scopus 로고
    • A Study on Sigmoid Kernels for SVM and the Training of Non-PSD Kernels by SMO-type Methods
    • National Taiwan University, Taipei, Taiwan
    • H.-T. Lin, C.-J. Lin, A Study on Sigmoid Kernels for SVM and the Training of Non-PSD Kernels by SMO-type Methods, Technical Report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, 2003.
    • (2003) Technical Report, Department of Computer Science and Information Engineering
    • Lin, H.-T.1    Lin, C.-J.2
  • 30
    • 31144448615 scopus 로고    scopus 로고
    • Using simulated annealing to optimize the feature selection problem in marketing applications
    • R. Meiri, and J. Zahavi Using simulated annealing to optimize the feature selection problem in marketing applications European Journal of Operational Research 171 June (3) 2006 842 858
    • (2006) European Journal of Operational Research , vol.171 , Issue.JUNE 3 , pp. 842-858
    • Meiri, R.1    Zahavi, J.2
  • 33
    • 0344291226 scopus 로고    scopus 로고
    • Recent approaches to global optimization problems through particle swarm optimization
    • K.E. Parsopoulos, and M.N. Vrahatis Recent approaches to global optimization problems through particle swarm optimization Natural Computing 1 June (2) 2002 235 306
    • (2002) Natural Computing , vol.1 , Issue.JUNE 2 , pp. 235-306
    • Parsopoulos, K.E.1    Vrahatis, M.N.2
  • 35
    • 0028547556 scopus 로고
    • Floating search methods in feature-selection
    • P. Pudil, J. Novovicova, and J. Kittler Floating search methods in feature-selection Pattern Recognition Letters 15 November (11) 1994 1119 1125
    • (1994) Pattern Recognition Letters , vol.15 , Issue.NOVEMBER 11 , pp. 1119-1125
    • Pudil, P.1    Novovicova, J.2    Kittler, J.3
  • 36
    • 0031139210 scopus 로고    scopus 로고
    • Optimization of computer simulation models with rare events
    • R. Rubinstein Optimization of computer simulation models with rare events European Journal of Operational Research 99 1997 89 112
    • (1997) European Journal of Operational Research , vol.99 , pp. 89-112
    • Rubinstein, R.1
  • 37
    • 0000228665 scopus 로고    scopus 로고
    • The cross-entropy method for combinatorial and continuous optimization
    • R. Rubinstein The cross-entropy method for combinatorial and continuous optimization Methodology and Computing in Applied Probability 1 1999 127 190
    • (1999) Methodology and Computing in Applied Probability , vol.1 , pp. 127-190
    • Rubinstein, R.1
  • 41
    • 0000629975 scopus 로고
    • Cross validation choice and assessment of statistical predictions
    • M. Stone Cross validation choice and assessment of statistical predictions Journal of the Royal Statistical Society B 36 1974 111 147
    • (1974) Journal of the Royal Statistical Society B , vol.36 , pp. 111-147
    • Stone, M.1
  • 42
    • 78049466915 scopus 로고    scopus 로고
    • Multi-objective optimization with cross entropy method: Stochastic learning with clustered Pareto fronts
    • A. Ünveren, A. Acan, Multi-objective optimization with cross entropy method: stochastic learning with clustered Pareto fronts, In: IEEE Congress on Evolutionary Computation, 2007, pp. 3065-3071.
    • (2007) IEEE Congress on Evolutionary Computation , pp. 3065-3071
    • Ünveren, A.1
  • 44
    • 33845523839 scopus 로고    scopus 로고
    • Feature selection based on rough sets and particle swarm optimization
    • X. Wang, J. Yang, X. Teng, W. Xia, and R. Jensen Feature selection based on rough sets and particle swarm optimization Pattern Recognition Letters 28 4 2007 459 471
    • (2007) Pattern Recognition Letters , vol.28 , Issue.4 , pp. 459-471
    • Wang, X.1    Yang, J.2    Teng, X.3    Xia, W.4    Jensen, R.5
  • 47
    • 84901401227 scopus 로고    scopus 로고
    • Cross entropy guided ant-like agents finding dependable primary/backup path patterns in networks
    • O. Wittner, B.E. Helvik, Cross entropy guided ant-like agents finding dependable primary/backup path patterns in networks, in: Congress on Evolutionary Computation (CEC2002), 2002.
    • (2002) Congress on Evolutionary Computation (CEC2002)
    • Wittner, O.1    Helvik, B.E.2
  • 48
    • 0032028297 scopus 로고    scopus 로고
    • Feature subset selection using a genetic algorithm
    • J. Yang, and V. Honavar Feature subset selection using a genetic algorithm IEEE Intelligent Systems 13 1998 44 49
    • (1998) IEEE Intelligent Systems , vol.13 , pp. 44-49
    • Yang, J.1    Honavar, V.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.