메뉴 건너뛰기




Volumn , Issue , 2011, Pages 1112-1119

Bio-inspired meta-heuristic as feature selector in ensemble systems: A comparative analysis

Author keywords

[No Author keywords available]

Indexed keywords

BIO-INSPIRED; COMBINATION METHOD; COMPARATIVE ANALYSIS; ENSEMBLE SYSTEMS; EXHAUSTIVE SEARCH; FEATURE SELECTION METHODS; HEURISTIC SEARCH; INDIVIDUAL CLASSIFIERS; META HEURISTICS; METAHEURISTIC; NP-HARD; OPTIMIZATION TECHNIQUES; PARTICLE SWARM; SEARCH PROBLEM;

EID: 80054763777     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2011.6033348     Document Type: Conference Paper
Times cited : (4)

References (28)
  • 1
    • 84948597805 scopus 로고
    • A comparison of seven techniques for choosing subsets of pattern recognition properties
    • Mucciardi, A; Gose, E. A Comparison of Seven Techniques for Choosing Subsets of Pattern Recognition Properties.IEEE Transactions on Computers. v. 20, n. 9, p. 1023-1031, 1971.
    • (1971) IEEE Transactions on Computers , vol.20 , Issue.9 , pp. 1023-1031
    • Mucciardi, A.1    Gose, E.2
  • 3
    • 0031078007 scopus 로고    scopus 로고
    • Feature selection: Evaluation, application, and small sample performance
    • Jain, A; Zongker, D. Feature selection: evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine. v. 19, n. 2, p. 153-158, 1997.
    • (1997) IEEE Transactions on Pattern Analysis and Machine , vol.19 , Issue.2 , pp. 153-158
    • Jain, A.1    Zongker, D.2
  • 4
    • 0002774069 scopus 로고
    • Feature set search algorithm
    • Chen, C (Org.) . Alphen aan den Rjin: Sithof and Noordhoff
    • Kittler, J. Feature set search algorithm. In: Chen, C (Org.). Pattern Recognition and Signal Processing. Alphen aan den Rjin: Sithof and Noordhoff, 1978. p. 41-60.
    • (1978) Pattern Recognition and Signal Processing , pp. 41-60
    • Kittler, J.1
  • 5
    • 0028547556 scopus 로고
    • Floating search methods in feature selection
    • Pudil, P; Novovicov, J; Kittler, J. Floating search methods in feature selection. Patt Recognition Letters. v. 15, n. 11, p. 1119-1125, 1994.
    • (1994) Patt Recognition Letters , vol.15 , Issue.11 , pp. 1119-1125
    • Pudil, P.1    Novovicov, J.2    Kittler, J.3
  • 6
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • Kohavi, R; John, G. Wrappers for feature subset selection. Artificial Intelligence. v. 97, n. 1, p. 271-324, 1997.
    • (1997) Artificial Intelligence , vol.97 , Issue.1 , pp. 271-324
    • Kohavi, R.1    John, G.2
  • 7
    • 0024895461 scopus 로고
    • A note on genetic algorithms for largescale feature selection
    • Siedlecki, W; Sklansky, J. A note on genetic algorithms for largescale feature selection. Patt Recognition Letters. 10(5), 335-347, 1989.
    • (1989) Patt Recognition Letters , vol.10 , Issue.5 , pp. 335-347
    • Siedlecki, W.1    Sklansky, J.2
  • 8
    • 33748076461 scopus 로고    scopus 로고
    • A GA-based feature selection and parameters optimization for support vector machines
    • Huang, C; Wang, C. A GA-based feature selection and parameters optimization for support vector machines. Expert Systems with Applications. v. 31, n. 2, p. 231-240, 2006.
    • (2006) Expert Systems with Applications , vol.31 , Issue.2 , pp. 231-240
    • Huang, C.1    Wang, C.2
  • 10
    • 41149138911 scopus 로고    scopus 로고
    • The ant colony algorithm for feature selection in high-dimension gene expression data for disease classification
    • Robbins, K; Zhang, W; Bertrand, J; Rekaya, R. The ant colony algorithm for feature selection in high-dimension gene expression data for disease classification. Mat Medicine & Bio. 24(4), 413-426, 2007.
    • (2007) Mat Medicine & Bio , vol.24 , Issue.4 , pp. 413-426
    • Robbins, K.1    Zhang, W.2    Bertrand, J.3    Rekaya, R.4
  • 11
    • 70349765925 scopus 로고    scopus 로고
    • Particle swarm for attribute selection in Bayesian classification: An application to protein function prediction
    • Correa, E; Freitas, A; Johnson, C. Particle swarm for attribute selection in Bayesian classification: an application to protein function prediction. Artificial Evolution and Applications, p. 1-12, 2008.
    • (2008) Artificial Evolution and Applications , pp. 1-12
    • Correa, E.1    Freitas, A.2    Johnson, C.3
  • 12
    • 17744402661 scopus 로고    scopus 로고
    • Feature subset selection by bayesian network-based optimization
    • Inza, I; LarraÒaga, P; Etxeberria, R; Sierra, B. Feature Subset Selection by Bayesian network-based optimization. Artificial Intelligence. v. 123, n. 1, p. 157-184, 2000.
    • (2000) Artificial Intelligence , vol.123 , Issue.1 , pp. 157-184
    • Inza, I.1    Larraòaga, P.2    Etxeberria, R.3    Sierra, B.4
  • 13
    • 0038137315 scopus 로고    scopus 로고
    • Ensemble feature selection with the simple Baysian classification
    • Tsymbal, A; Puuronen, S; Patterson, D. Ensemble feature selection with the simple Baysian classification. Inf Fusion, 4(2), 87-100, 2003.
    • (2003) Inf Fusion , vol.4 , Issue.2 , pp. 87-100
    • Tsymbal, A.1    Puuronen, S.2    Patterson, D.3
  • 15
    • 54549099006 scopus 로고    scopus 로고
    • Performance of feature selection methods in the classification of high-dimension data
    • Hua, J; Tembe, W; Dougherty, E. Performance of feature selection methods in the classification of high-dimension data. Pattern Recognition. v. 42, n. 3, p. 409-424, 2009.
    • (2009) Pattern Recognition , vol.42 , Issue.3 , pp. 409-424
    • Hua, J.1    Tembe, W.2    Dougherty, E.3
  • 17
    • 0003401932 scopus 로고
    • Optimization learning and natural algorithms
    • Thesis of Doctorate-Dipartimento di Elettronica, Politecnico di Milano, Milan, 1992
    • Dorigo, M. Optimization, learning and natural algorithms. Milan: Politecnico di Milano, 1992. Thesis of Doctorate-Dipartimento di Elettronica, Politecnico di Milano, Milan, 1992.
    • (1992) Milan: Politecnico di Milano
    • Dorigo, M.1
  • 19
    • 80054769952 scopus 로고    scopus 로고
    • Otimização por colônia de partículas
    • Saramago, S; Prado, J. Otimização por colônia de partículas. FAMAT em revista. v. 4, p. 87-103, 2005.
    • (2005) FAMAT em Revista , vol.4 , pp. 87-103
    • Saramago, S.1    Prado, J.2
  • 22
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • Demsar, J. Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research. v. 7, p. 1-30, 2006.
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1-30
    • Demsar, J.1
  • 23
    • 78649934709 scopus 로고    scopus 로고
    • Irvine CA: University of California, School of Information and Computer Science, 2010
    • Frank, A; Asuncion, A.UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science, 2010.Available in: http://archive.ics.uci.edu/ml/.
    • Asuncion A.UCI Machine Learning Repository
    • Frank, A.1
  • 25
    • 58249093836 scopus 로고    scopus 로고
    • A feature selection technique for generation of classification committees and its application to categorization of laryngeal images
    • Bacauskiene, M;Verikas, A;Gelzinisa, A;Valinciusa, D. A feature selection technique for generation of classification committees and its application to categorization of laryngeal images. Patt Rec Let. 42(5), 645-654, 2009.
    • (2009) Patt Rec Let. , vol.42 , Issue.5 , pp. 645-654
    • Bacauskiene, M.1    Verikas, A.2    Gelzinisa, A.3    Valinciusa, D.4
  • 26
    • 33747425207 scopus 로고    scopus 로고
    • Feature selection for ensembles applied to handwriting recognition
    • Oliveira, L; Morita, M; Sabourin, R. Feature selection for ensembles applied to handwriting recognition. Int J of Doc Analysis, 262-279, 2006.
    • (2006) Int J of Doc Analysis , pp. 262-279
    • Oliveira, L.1    Morita, M.2    Sabourin, R.3
  • 27
    • 0038724494 scopus 로고    scopus 로고
    • Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data
    • S. Monti, P. Tamayo, J. Mesirov, and T. Golub. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learning, vol.52:91-118, 2003.
    • (2003) Machine Learning , vol.52 , pp. 91-118
    • Monti, S.1    Tamayo, P.2    Mesirov, J.3    Golub, T.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.