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Volumn , Issue , 2010, Pages

Using evolutionary computation to improve SVM classification

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; CRITICAL DESIGN; DECISION PROBLEMS; DESIGN GUIDANCE; FEATURE MAPPING; FEATURE SETS; FEATURE SPACE; GENERIC FEATURES; HIGHER-DIMENSIONAL; KERNEL FUNCTION; MACHINE LEARNING TECHNIQUES; SVM CLASSIFICATION;

EID: 79959423743     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2010.5586432     Document Type: Conference Paper
Times cited : (11)

References (36)
  • 1
    • 0026966646 scopus 로고
    • A training algorithm for optimal margin classifiers
    • D. Haussler, editor, ACM Press
    • B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In D. Haussler, editor, 5th Annual ACM Workshop on COLT, pages 144-152. ACM Press, 1992.
    • (1992) 5th Annual ACM Workshop on COLT , pp. 144-152
    • Boser, B.E.1    Guyon, I.M.2    Vapnik, V.N.3
  • 2
    • 0742301681 scopus 로고    scopus 로고
    • Mismatch string kernels for SVM protein classification
    • L. C., E. E., A. Cohen, A. Weston, and W. S. Noble. Mismatch string kernels for SVM protein classification. Neur. Inf. Proc. Sys., 15(4):1441-1448, 2002.
    • (2002) Neur. Inf. Proc. Sys. , vol.15 , Issue.4 , pp. 1441-1448
    • C, L.1    E, E.2    Cohen, A.3    Weston, A.4    Noble, W.S.5
  • 3
    • 27144519762 scopus 로고    scopus 로고
    • Feature subset selection, class separability, and genetic algorithms
    • Springer Berlin / Heidelberg
    • E. Cantu-Paz. Feature subset selection, class separability, and genetic algorithms. In Proceedings of GECCO-2004, pages 959-970. Springer Berlin / Heidelberg, 2004.
    • (2004) Proceedings of GECCO-2004 , pp. 959-970
    • Cantu-Paz, E.1
  • 7
    • 29144499905 scopus 로고    scopus 로고
    • Working set selection using the second order information for training SVM
    • R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using the second order information for training SVM. J. Mach. Learn. Res., 6(1532-4435):1889-1918, 2005.
    • (2005) J. Mach. Learn. Res. , vol.6 , Issue.1532-4435 , pp. 1889-1918
    • Fan, R.-E.1    Chen, P.-H.2    Lin, C.-J.3
  • 8
    • 0030581149 scopus 로고    scopus 로고
    • Chromatin unfolds
    • DOI 10.1016/S0092-8674(00)80073-2
    • G. Felsenfeld. Chromatin unfolds. Cell, 86(1):13-19, 1996. (Pubitemid 26256574)
    • (1996) Cell , vol.86 , Issue.1 , pp. 13-19
    • Felsenfeld, G.1
  • 10
    • 44449101758 scopus 로고    scopus 로고
    • Supervised learning method for the prediction of subcellular localization of proteins using amino acid and amino acid pair composition
    • T. Habib, C. Zhang, J. Y. Yang, M. Q. Yang, and Y. Deng. Supervised learning method for the prediction of subcellular localization of proteins using amino acid and amino acid pair composition. BMC Genom., 9(Suppl1):S1-S16, 2008.
    • (2008) BMC Genom. , vol.9 , Issue.SUPPL. 1
    • Habib, T.1    Zhang, C.2    Yang, J.Y.3    Yang, M.Q.4    Deng, Y.5
  • 11
    • 0020083498 scopus 로고
    • The meaning and use of the area under a receiver operating characteristic (ROC) curve
    • J. A. Hanley and B. J. McNeil. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143:29-36, 1982.
    • (1982) Radiology , vol.143 , pp. 29-36
    • Hanley, J.A.1    McNeil, B.J.2
  • 13
    • 2942517813 scopus 로고    scopus 로고
    • cis-Regulatory control circuits in development
    • M. L. Howard and E. Davidson. cis-Regulatory control circuits in development. Dev. Biol., 271(1):109-118, 2004.
    • (2004) Dev. Biol. , vol.271 , Issue.1 , pp. 109-118
    • Howard, M.L.1    Davidson, E.2
  • 14
    • 77955894907 scopus 로고    scopus 로고
    • A feature generation algorithm with applications to biological sequence classification
    • H. Liu and H. Motoda, editors, Springer, Berling, Heiderlberg
    • R. Islamaj-Dogan, L. Getoor, and W. J. Wilbur. A feature generation algorithm with applications to biological sequence classification. In H. Liu and H. Motoda, editors, Computational Methods of Feature Selection. Springer, Berling, Heiderlberg, 2007.
    • (2007) Computational Methods of Feature Selection
    • Islamaj-Dogan, R.1    Getoor, L.2    Wilbur, W.J.3
  • 17
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • R. Kohavi and G. H. 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.H.2
  • 20
    • 70449381272 scopus 로고    scopus 로고
    • Optimization of combined kernel function for svm by particle swarm optimization
    • Baoding, China
    • M.-Z. Lu, C. L. P. Chen, and J.-B. Huo. Optimization of combined kernel function for svm by particle swarm optimization. In IEEE Intl Conf on Machine Learning and Cybernetics, volume 2, pages 1160-1166, Baoding, China, 2009.
    • (2009) IEEE Intl Conf on Machine Learning and Cybernetics , vol.2 , pp. 1160-1166
    • Lu, M.-Z.1    Chen, C.L.P.2    Huo, J.-B.3
  • 21
    • 70049117806 scopus 로고    scopus 로고
    • Optimization of combined kernel function for svm based on large margin learning theory
    • Singapore
    • M.-Z. Lu, C. L. P. Chen, J.-B. Huo, and X. Wang. Optimization of combined kernel function for svm based on large margin learning theory. In IEEE Intl Conf on Systems, Man and Cybernetics, pages 353-358, Singapore, 2008.
    • (2008) IEEE Intl Conf on Systems, Man and Cybernetics , pp. 353-358
    • Lu, M.-Z.1    Chen, C.L.P.2    Huo, J.-B.3    Wang, X.4
  • 22
    • 29144460132 scopus 로고    scopus 로고
    • Predicting the in vivo signature of human gene regulatory sequences
    • W. S. Noble, S. Kuehn, R. Thurman, M. Yu, and J. A. Stamatoyannopoulos. Predicting the in vivo signature of human gene regulatory sequences. Bioinformatics, 21(Suppl 1):i338-i343, 2005.
    • (2005) Bioinformatics , vol.21 , Issue.SUPPL. 1
    • Noble, W.S.1    Kuehn, S.2    Thurman, R.3    Yu, M.4    Stamatoyannopoulos, J.A.5
  • 23
    • 24944435114 scopus 로고    scopus 로고
    • Determining optimal decision model for support vector machine by genetic algorithm
    • J. Zhang, J.-H. He, and F. Y., editors, Springer
    • S.-Y. Ohn, H.-N. Nguyen, D. S. Kim, and J. S. Park. Determining optimal decision model for support vector machine by genetic algorithm. In J. Zhang, J.-H. He, and F. Y., editors, Lecture Notes in Computer Science: Computational and Information Science, volume 3314, pages 895-902. Springer, 2005.
    • (2005) Lecture Notes in Computer Science: Computational and Information Science , vol.3314 , pp. 895-902
    • Ohn, S.-Y.1    Nguyen, H.-N.2    Kim, D.S.3    Park, J.S.4
  • 24
    • 0035282695 scopus 로고    scopus 로고
    • Genesplicer: A new computational method for splice site prediction
    • M. Pertea, X. Lin, and S. L. Salzberg. Genesplicer: a new computational method for splice site prediction. Nucl. Acids Res., 29(5):1185-1190, 2001.
    • (2001) Nucl. Acids Res. , vol.29 , Issue.5 , pp. 1185-1190
    • Pertea, M.1    Lin, X.2    Salzberg, S.L.3
  • 26
    • 79959427208 scopus 로고    scopus 로고
    • SAS Institute Inc. Version 7
    • SAS Institute Inc. JMP Version 7, 1998-2007.
    • (1998) JMP
  • 27
    • 0021760026 scopus 로고
    • Computer methods to locate signals in nucleic acid sequences
    • R. Staden. Computer methods to locate signals in nucleic acid sequences. Nucl. Acids Res., 12(1):505-519, 1984.
    • (1984) Nucl. Acids Res. , vol.12 , Issue.1 , pp. 505-519
    • Staden, R.1
  • 28
    • 0028853170 scopus 로고
    • NF-E2 and GATA binding motifs are required for the formation of DNase I hypersensitive site 4 of the human beta-globin locus control region
    • J. A. Stamatoyannopoulos, A. Goodwin, T. Joyce, and C. H. Lowrey. NF-E2 and GATA binding motifs are required for the formation of DNase I hypersensitive site 4 of the human beta-globin locus control region. EMBO J., 14(1):106-116, 1995.
    • (1995) EMBO J. , vol.14 , Issue.1 , pp. 106-116
    • Stamatoyannopoulos, J.A.1    Goodwin, A.2    Joyce, T.3    Lowrey, C.H.4
  • 29
    • 85027109147 scopus 로고
    • Genetic algorithms as a tool for feature selection in machine learning
    • Society Press
    • H. Vafaie and K. D. Jong. Genetic algorithms as a tool for feature selection in machine learning. In Proceedings of Conference on Tools for AI 1992, pages 200-204. Society Press, 1992.
    • (1992) Proceedings of Conference on Tools for AI 1992 , pp. 200-204
    • Vafaie, H.1    Jong, K.D.2
  • 33
    • 0032028297 scopus 로고    scopus 로고
    • Feature subset selection using a genetic algorithm
    • J. Yang and V. Honavar. Feature subset selection using a genetic algorithm. Intelligent Systems, 13(2):44-49, 1998.
    • (1998) Intelligent Systems , vol.13 , Issue.2 , pp. 44-49
    • Yang, J.1    Honavar, V.2
  • 35
    • 2442441507 scopus 로고    scopus 로고
    • Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals
    • G. Yeo and C. Burge. Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J. Comp. Biol., 11(2-3):377-394, 2003.
    • (2003) J. Comp. Biol. , vol.11 , Issue.2-3 , pp. 377-394
    • Yeo, G.1    Burge, C.2
  • 36
    • 0027377256 scopus 로고
    • A weight array method for splicing signal analysis
    • M. Q. Zhang and T. G. Marr. A weight array method for splicing signal analysis. Comput. Appl. Biosci., 9(5):499-509, 1993.
    • (1993) Comput. Appl. Biosci. , vol.9 , Issue.5 , pp. 499-509
    • Zhang, M.Q.1    Marr, T.G.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.