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Volumn , Issue , 2009, Pages 1027-1034

Pareto front feature selection: Using genetic programming to explore feature space

Author keywords

Feature subset selection; Filter approach; Genetic programming; Pareto front

Indexed keywords

BINARY CLASSIFICATION; CLASSIFICATION PERFORMANCE; CONTROL MECHANISM; FEATURE SELECTION; FEATURE SPACE; FEATURE SUBSET SELECTION; FILTER APPROACH; FILTER METHOD; LINEAR SEARCH; MATHEMATICAL EXPRESSIONS; OVERFITTING; PARETO FRONT; RUNTIMES; SEARCH SPACES;

EID: 72749084593     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1569901.1570040     Document Type: Conference Paper
Times cited : (47)

References (27)
  • 2
    • 72749094125 scopus 로고    scopus 로고
    • A. Asuncion and D. Newman. UCI ML repository. http://archive.ics.uci.edu/ ml/index.html, 2007.
    • A. Asuncion and D. Newman. UCI ML repository. http://archive.ics.uci.edu/ ml/index.html, 2007.
  • 3
    • 33750584880 scopus 로고    scopus 로고
    • Logistic regression for feature selection and soft classification of remote sensing data
    • Q. Cheng, P. Varshney, and M. Arora. Logistic regression for feature selection and soft classification of remote sensing data. Geoscience and Remote Sensing Letters, IEEE, 3:491-494, 2006.
    • (2006) Geoscience and Remote Sensing Letters, IEEE , vol.3 , pp. 491-494
    • Cheng, Q.1    Varshney, P.2    Arora, M.3
  • 5
    • 0031904206 scopus 로고    scopus 로고
    • Genetic programming for classification and feature selection: Analysis of 1h nuclear magnetic resonance spectra from human brain tumour biopsies
    • H. F. Gray. Genetic programming for classification and feature selection: analysis of 1h nuclear magnetic resonance spectra from human brain tumour biopsies. NMR in Biomedicine, 11:217-224, 1998.
    • (1998) NMR in Biomedicine , vol.11 , pp. 217-224
    • Gray, H.F.1
  • 6
    • 85065703189 scopus 로고    scopus 로고
    • Correlation-based feature selection for discrete and numeric class machine learning
    • Morgan Kaufmann Publishers Inc, USA
    • M. A. Hall. Correlation-based feature selection for discrete and numeric class machine learning. In Proceedings of the Seventeenth International Conference on Machine Learning, pages 359-366. Morgan Kaufmann Publishers Inc., USA, 2000.
    • (2000) Proceedings of the Seventeenth International Conference on Machine Learning , pp. 359-366
    • Hall, M.A.1
  • 9
    • 0000545946 scopus 로고    scopus 로고
    • Improvements to platt's smo algorithm for svm classifier design
    • Mar
    • S. S. Keerthi, S. K. Shevade, C. Bhattacharyya, and K. R. K. Murthy. Improvements to platt's smo algorithm for svm classifier design. Neural Comp., 13:637-649, Mar. 2001.
    • (2001) Neural Comp , vol.13 , pp. 637-649
    • Keerthi, S.S.1    Shevade, S.K.2    Bhattacharyya, C.3    Murthy, K.R.K.4
  • 10
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • R. Kohavi and G. John. Wrappers for feature subset selection. Artificial Intelligence, 97:273-324, 1997.
    • (1997) Artificial Intelligence , vol.97 , pp. 273-324
    • Kohavi, R.1    John, G.2
  • 13
    • 58349115958 scopus 로고    scopus 로고
    • Discriminant feature selection by genetic programming: Towards a domain independent multi-class object detection system
    • J. A. Landry, L. D. Costa, and T. Bernier. Discriminant feature selection by genetic programming: Towards a domain independent multi-class object detection system. Systemics, Cybernetics and Informatics, 3:76-81, 2006.
    • (2006) Systemics, Cybernetics and Informatics , vol.3 , pp. 76-81
    • Landry, J.A.1    Costa, L.D.2    Bernier, T.3
  • 14
    • 0035336996 scopus 로고    scopus 로고
    • M. Last, A. K, and O. Maimon. Information-theoretic algorithm for feature selection. Pattern Recognition Letters, 22:799-811, 2001.
    • M. Last, A. K, and O. Maimon. Information-theoretic algorithm for feature selection. Pattern Recognition Letters, 22:799-811, 2001.
  • 15
    • 36148943944 scopus 로고    scopus 로고
    • Classifier design with feature selection and feature extraction using layered genetic programming
    • J.-Y. Lin, H.-R. Ke, B.-C. Chien, and W.-P. Yang. Classifier design with feature selection and feature extraction using layered genetic programming. Expert Syst. Appl., 34:1384-1393, 2008.
    • (2008) Expert Syst. Appl , vol.34 , pp. 1384-1393
    • Lin, J.-Y.1    Ke, H.-R.2    Chien, B.-C.3    Yang, W.-P.4
  • 17
    • 58349113039 scopus 로고    scopus 로고
    • Genetic programming for feature ranking in classification problems
    • Proceedings of the seventh International Conference on Simulated Evolution and Learning SEAL'08, of, Australia, Springer
    • K. Neshatian and M. Zhang. Genetic programming for feature ranking in classification problems. In Proceedings of the seventh International Conference on Simulated Evolution and Learning (SEAL'08), volume 5361 of Lecture Notes in Computer Science, Australia, 2008. Springer.
    • (2008) Lecture Notes in Computer Science , vol.5361
    • Neshatian, K.1    Zhang, M.2
  • 21
    • 35048842196 scopus 로고    scopus 로고
    • S. Silva and E. Costa. Dynamic limits for bloat control: Variations on size and depth. In K. D. et al, editor, Genetic and Evolutionary Computation - GECCO-2004, Part II, 3103 of Lecture Notes in Computer Science, pages 666-677, Seattle, WA, USA, 2004. Springer-Verlag.
    • S. Silva and E. Costa. Dynamic limits for bloat control: Variations on size and depth. In K. D. et al, editor, Genetic and Evolutionary Computation - GECCO-2004, Part II, volume 3103 of Lecture Notes in Computer Science, pages 666-677, Seattle, WA, USA, 2004. Springer-Verlag.
  • 22
    • 27944437848 scopus 로고    scopus 로고
    • M. G. Smith and L. Bull. Genetic programming with a genetic algorithm for feature construction and selection. Genetic Programming and Evolvable Machines, 6:265-281, Sept. 2005. Published online: 17 August 2005.
    • M. G. Smith and L. Bull. Genetic programming with a genetic algorithm for feature construction and selection. Genetic Programming and Evolvable Machines, 6:265-281, Sept. 2005. Published online: 17 August 2005.
  • 23
    • 34547376690 scopus 로고    scopus 로고
    • Variable selection in industrial datasets using pareto genetic programming
    • Springer, Ann Arbor
    • G. Smits, A. Kordon, K. Vladislavleva, E. Jordaan, and M. Kotanchek. Variable selection in industrial datasets using pareto genetic programming. Genetic Programming, 9:79-92. Springer, Ann Arbor, 2005.
    • (2005) Genetic Programming , vol.9 , pp. 79-92
    • Smits, G.1    Kordon, A.2    Vladislavleva, K.3    Jordaan, E.4    Kotanchek, M.5
  • 25
    • 34548058452 scopus 로고
    • and the genetic construction of computer programs. PhD thesis, University of Southern California
    • W. A. Tackett. Recombination, selection, and the genetic construction of computer programs. PhD thesis, University of Southern California, 1994.
    • (1994) Recombination, selection
    • Tackett, W.A.1
  • 27
    • 25144492516 scopus 로고    scopus 로고
    • Efficient feature selection via analysis of relevance and redundancy
    • L. Yu and H. Liu. Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research, 5: 1205-1224, 2004.
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 1205-1224
    • Yu, L.1    Liu, H.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.