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Volumn 21, Issue 5, 2007, Pages 809-830

A methodology for improving the performance of non-ranker feature selection filters

Author keywords

Ensemble methodology; Feature selection; Pattern classification

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL EFFICIENCY; DATA REDUCTION; LEARNING ALGORITHMS;

EID: 34547761741     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001407005727     Document Type: Article
Times cited : (23)

References (35)
  • 1
    • 0025725905 scopus 로고
    • Instance-based learning algorithms
    • D. Aha and D. Kibler, Instance-based learning algorithms, Mach. Learn. 6 (1991) 37-66.
    • (1991) Mach. Learn , vol.6 , pp. 37-66
    • Aha, D.1    Kibler, D.2
  • 2
    • 0002094343 scopus 로고    scopus 로고
    • Generalization performance of support vector machines and other pattern classifiers
    • eds. B. Scholkopf, C. J. C. Burges and A. J. Smola MIT Press, Cambridge, USA
    • P. Bartlett and J. Shawe-Taylor, Generalization performance of support vector machines and other pattern classifiers, Advances in Kernel Methods, Support Vector Learning, eds. B. Scholkopf, C. J. C. Burges and A. J. Smola (MIT Press, Cambridge, USA, 1998), pp. 43-54.
    • (1998) Advances in Kernel Methods, Support Vector Learning , pp. 43-54
    • Bartlett, P.1    Shawe-Taylor, J.2
  • 3
    • 0043245810 scopus 로고    scopus 로고
    • Boosting with $L.28 loss: Regression and classification
    • P. Buhlmann and B. Yu, Boosting with $L.28 loss: regression and classification, J. Amer. Statist. Assoc. 98 (2003) 324-338.
    • (2003) J. Amer. Statist. Assoc , vol.98 , pp. 324-338
    • Buhlmann, P.1    Yu, B.2
  • 4
    • 0000521473 scopus 로고
    • Ridge estimators in logistic regression
    • S. le Cessie and J. C. van Houwelingen, Ridge estimators in logistic regression, Appl. Stat. 41(1) (1992) 191-201.
    • (1992) Appl. Stat , vol.41 , Issue.1 , pp. 191-201
    • le Cessie, S.1    van Houwelingen, J.C.2
  • 6
    • 84974722422 scopus 로고    scopus 로고
    • P. Cunningham and J. Carney, Diversity versus quality in classification ensembles based on feature selection, Proc. ECML 2000, 11th European Conf. Machine Learning, Barcelona, Spain, eds. R. L. de Mántaras and E. Plaza, Lecture Notes in Computer Science, 1810 (Springer), 2000, pp. 109-116.
    • P. Cunningham and J. Carney, Diversity versus quality in classification ensembles based on feature selection, Proc. ECML 2000, 11th European Conf. Machine Learning, Barcelona, Spain, eds. R. L. de Mántaras and E. Plaza, Lecture Notes in Computer Science, Vol. 1810 (Springer), 2000, pp. 109-116.
  • 12
    • 0032139235 scopus 로고    scopus 로고
    • The random subspace method for constructing decision forests
    • T. K. Ho, The random subspace method for constructing decision forests, IEEE Trans. Patt. Anal. Mach. Intell. 20(8) (1998) 832-844.
    • (1998) IEEE Trans. Patt. Anal. Mach. Intell , vol.20 , Issue.8 , pp. 832-844
    • Ho, T.K.1
  • 13
    • 0027580356 scopus 로고
    • Very simple classification rules perform well on most commonly used datasets
    • R. C. Holte, Very simple classification rules perform well on most commonly used datasets, Mach. Learn. 11 (1993) 63-91.
    • (1993) Mach. Learn , vol.11 , pp. 63-91
    • Holte, R.C.1
  • 14
    • 4544223395 scopus 로고    scopus 로고
    • Using rough sets theory and database operations to construct a good ensemble of classifiers for data mining applications
    • X. Hu, Using rough sets theory and database operations to construct a good ensemble of classifiers for data mining applications, ICDM01 (2001), pp. 233-240.
    • (2001) ICDM01 , pp. 233-240
    • Hu, X.1
  • 15
    • 0000661829 scopus 로고
    • An exploratory technique for investigating large quantities of categorical data
    • G. V. Kass, An exploratory technique for investigating large quantities of categorical data, Appl. Stat. 29(2) (1980) 119-127.
    • (1980) Appl. Stat , vol.29 , Issue.2 , pp. 119-127
    • Kass, G.V.1
  • 16
    • 34547818521 scopus 로고    scopus 로고
    • J. Kittler, Feature set search algorithms, Pattern Recognition and Signal Processing, ed. C. H. Chen, Sijthoff and Noordhoff (Alphen ann den Rijn, The Netherlands, 1978), pp. 41-60.
    • J. Kittler, Feature set search algorithms, Pattern Recognition and Signal Processing, ed. C. H. Chen, Sijthoff and Noordhoff (Alphen ann den Rijn, The Netherlands, 1978), pp. 41-60.
  • 17
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • R. Kohavi and G. John, Wrappers for feature subset selection, Artif. Intel. 97(1-2) (1996) 273-324.
    • (1996) Artif. Intel , vol.97 , Issue.1-2 , pp. 273-324
    • Kohavi, R.1    John, G.2
  • 20
    • 33748914421 scopus 로고    scopus 로고
    • Random Voronoi ensembles for gene selection in DNA microarray data
    • eds. U. Seiffert and L. C. Jain World Scientific, Singapore
    • F. Masulli and S. Rovetta, Random Voronoi ensembles for gene selection in DNA microarray data, Bioinformatics using Computational Intelligence Paradigms, eds. U. Seiffert and L. C. Jain (World Scientific, Singapore, 2003).
    • (2003) Bioinformatics using Computational Intelligence Paradigms
    • Masulli, F.1    Rovetta, S.2
  • 22
    • 0142086622 scopus 로고    scopus 로고
    • A methodology for feature selection using multi-objective genetic algorithms for handwritten digit string recognition
    • L. S. Oliveira, R. Sabourin, F. Bortolozzi and C. Y. Suen, A methodology for feature selection using multi-objective genetic algorithms for handwritten digit string recognition, Int. J. Patt. Recogn. Artif. Intell. 17(6) (2003) 903-930.
    • (2003) Int. J. Patt. Recogn. Artif. Intell , vol.17 , Issue.6 , pp. 903-930
    • Oliveira, L.S.1    Sabourin, R.2    Bortolozzi, F.3    Suen, C.Y.4
  • 23
    • 24344486891 scopus 로고    scopus 로고
    • Multi-objective genetic algorithms to create ensemble of classifiers
    • Proc. Third Int. Conf. Evolutionary Multi-Criterion Optimization EMO 2005, Guanajuato, Mexico, March 9-11, Springer
    • L. S. Oliveira, M. E. Morita, R. Sabourin and F. Bortolozzi, Multi-objective genetic algorithms to create ensemble of classifiers, Proc. Third Int. Conf. Evolutionary Multi-Criterion Optimization (EMO 2005), Guanajuato, Mexico, March 9-11, 2005, Lecture Notes in Computer Science, Vol. 3410 (Springer, 2005), pp. 592-606.
    • (2005) Lecture Notes in Computer Science , vol.3410 , pp. 592-606
    • Oliveira, L.S.1    Morita, M.E.2    Sabourin, R.3    Bortolozzi, F.4
  • 27
  • 29
    • 33744584654 scopus 로고    scopus 로고
    • Induction of decision trees
    • J. Quinlan, Induction of decision trees, Mach. Learn. 1 (1996) 81-106.
    • (1996) Mach. Learn , vol.1 , pp. 81-106
    • Quinlan, J.1
  • 30
    • 30344481918 scopus 로고    scopus 로고
    • Variable selection using ensemble methods
    • K. Torkkola and E. Tuv, Variable selection using ensemble methods, IEEE Intell. Syst. 20(6) (2005) 68-70.
    • (2005) IEEE Intell. Syst , vol.20 , Issue.6 , pp. 68-70
    • Torkkola, K.1    Tuv, E.2
  • 31
    • 0038137315 scopus 로고    scopus 로고
    • Ensemble feature selection with the simple Bayesian classification
    • A. Tsymbal, S. Puuronen and D. Petterson, Ensemble feature selection with the simple Bayesian classification, Information Fusion 4(2) (2003) 87-100.
    • (2003) Information Fusion , vol.4 , Issue.2 , pp. 87-100
    • Tsymbal, A.1    Puuronen, S.2    Petterson, D.3
  • 32
    • 0030365938 scopus 로고    scopus 로고
    • Error correlation and error reduction in ensemble classifiers
    • K. Turner and J. Ghosh, Error correlation and error reduction in ensemble classifiers, Connect. Sci. 8(3-4) (1999) 385-404.
    • (1999) Connect. Sci , vol.8 , Issue.3-4 , pp. 385-404
    • Turner, K.1    Ghosh, J.2
  • 35
    • 0032028297 scopus 로고    scopus 로고
    • J. Yang and V. Honavar, Feature subset selection using a genetic algorithm, IEEE Intell. Syst. (Special Issue on Feature Transformation and Subset Selection) 13(2) (1998) 44-49.
    • J. Yang and V. Honavar, Feature subset selection using a genetic algorithm, IEEE Intell. Syst. (Special Issue on Feature Transformation and Subset Selection) 13(2) (1998) 44-49.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.