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Volumn 19, Issue 3, 2008, Pages 431-441

Performing feature selection with multilayer perceptrons

Author keywords

Experimental work; Feature selection (FS); Multilayer perceptrons; Wrapper approach

Indexed keywords

COMPUTATIONAL METHODS; DECISION THEORY; FEATURE EXTRACTION;

EID: 40949143180     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2007.909535     Document Type: Article
Times cited : (96)

References (48)
  • 1
  • 3
    • 0003408496 scopus 로고    scopus 로고
    • Dept. Inf. Comput. Sci, Univ. California, Irvine, CA, Online, Available
    • C. L. Blake and C. J. Merz, "UCI repository of machine learning databases," Dept. Inf. Comput. Sci., Univ. California, Irvine, CA, 1998 [Online]. Available: http://www.ics.uci.edu/mlearn/MLRepository.html
    • (1998) UCI repository of machine learning databases
    • Blake, C.L.1    Merz, C.J.2
  • 4
    • 0031334221 scopus 로고    scopus 로고
    • Selection of relevant features and examples in machine learning
    • A. L. Blum and P. Langley, "Selection of relevant features and examples in machine learning," Artif. Intell., vol. 97, no. 1-2, pp. 245-271, 1997.
    • (1997) Artif. Intell , vol.97 , Issue.1-2 , pp. 245-271
    • Blum, A.L.1    Langley, P.2
  • 5
    • 0024771664 scopus 로고
    • Orthogonal least squares methods and their applications to non-linear system identification
    • S. Chen, S. A. Billings, and W. Luo, "Orthogonal least squares methods and their applications to non-linear system identification," Int. J. Control, vol. 50, no. 5, pp. 1873-1896, 1989.
    • (1989) Int. J. Control , vol.50 , Issue.5 , pp. 1873-1896
    • Chen, S.1    Billings, S.A.2    Luo, W.3
  • 7
    • 0030608136 scopus 로고    scopus 로고
    • Variable selection with neural networks
    • T. Cibas, F. F. Soulié, P. Gallinari, and Š. Raudys, "Variable selection with neural networks," Neurocomputing, vol. 12, no. 2-3, pp. 223-248, 1996.
    • (1996) Neurocomputing , vol.12 , Issue.2-3 , pp. 223-248
    • Cibas, T.1    Soulié, F.F.2    Gallinari, P.3    Raudys, S.4
  • 8
    • 0012937288 scopus 로고    scopus 로고
    • A unified bias-variance decomposition and its applications
    • P. Domingos, "A unified bias-variance decomposition and its applications," in Proc. 17th Int. Conf. Mach. Learn., 2000, pp. 231-238.
    • (2000) Proc. 17th Int. Conf. Mach. Learn , pp. 231-238
    • Domingos, P.1
  • 9
    • 0033220785 scopus 로고    scopus 로고
    • Sequential selection of discrete features for neural networks - A Bayesian approach to building a cascade
    • M. Egmont-Petersen, W. R. M. Dassen, and J. H. C. Reiber, "Sequential selection of discrete features for neural networks - A Bayesian approach to building a cascade," Pattern Recognit. Lett., vol. 20, no. 11-13, pp. 1439-1448, 1999.
    • (1999) Pattern Recognit. Lett , vol.20 , Issue.11-13 , pp. 1439-1448
    • Egmont-Petersen, M.1    Dassen, W.R.M.2    Reiber, J.H.C.3
  • 10
    • 0032100159 scopus 로고    scopus 로고
    • Assessing the importance of features for multi-layer perceptrons
    • M. Egmont-Petersen, J. L. Talmon, A. Hasman, and A. W. Ambergen, "Assessing the importance of features for multi-layer perceptrons," Neural Netw., vol. 11, no. 4, pp. 623-635, 1998.
    • (1998) Neural Netw , vol.11 , Issue.4 , pp. 623-635
    • Egmont-Petersen, M.1    Talmon, J.L.2    Hasman, A.3    Ambergen, A.W.4
  • 11
    • 0035505658 scopus 로고    scopus 로고
    • A new pruning heuristic based on variance analysis of sensitivity information
    • Nov
    • A. P. Engelbrecht, "A new pruning heuristic based on variance analysis of sensitivity information," IEEE Trans. Neural Netw., vol. 12, no. 6, pp. 1386-1399, Nov. 2001.
    • (2001) IEEE Trans. Neural Netw , vol.12 , Issue.6 , pp. 1386-1399
    • Engelbrecht, A.P.1
  • 12
    • 0001942829 scopus 로고
    • Neural networks and the bias/variance dilemma
    • S. Geman, E. Bienenstock, and R. Doursat, "Neural networks and the bias/variance dilemma," Neural Comput., vol. 4, no. 1, pp. 1-58, 1992.
    • (1992) Neural Comput , vol.4 , Issue.1 , pp. 1-58
    • Geman, S.1    Bienenstock, E.2    Doursat, R.3
  • 13
    • 0010641201 scopus 로고    scopus 로고
    • Anisotropic noise injection for input variables relevance determination
    • Nov
    • Y. Grandvalet, "Anisotropic noise injection for input variables relevance determination," IEEE Trans. Neural Netw., vol. 11, no. 6, pp. 1201-1212, Nov. 2000.
    • (2000) IEEE Trans. Neural Netw , vol.11 , Issue.6 , pp. 1201-1212
    • Grandvalet, Y.1
  • 14
    • 33745561205 scopus 로고    scopus 로고
    • An introduction to variable and feature selection
    • I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," J. Mach. Learn. Res., vol. 3, pp. 1157-1182, 2003.
    • (2003) J. Mach. Learn. Res , vol.3 , pp. 1157-1182
    • Guyon, I.1    Elisseeff, A.2
  • 15
    • 84898964855 scopus 로고    scopus 로고
    • Result analysis of the NIPS 2003 feature selection challenge
    • Cambridge, MA: MIT Press
    • I. Guyon, S. Gunn, A. Ben-Hur, and G. Dror, "Result analysis of the NIPS 2003 feature selection challenge," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2005, vol. 17, pp. 545-552.
    • (2005) Advances in Neural Information Processing Systems , vol.17 , pp. 545-552
    • Guyon, I.1    Gunn, S.2    Ben-Hur, A.3    Dror, G.4
  • 16
    • 0001234705 scopus 로고
    • Second order derivatives for network pruning: Optimal brain surgeon
    • San Mateo, CA: Morgan Kaufmann
    • B. Hassibi and D. G. Stork, "Second order derivatives for network pruning: Optimal brain surgeon," in Advances in Neural Information Processing Systems. San Mateo, CA: Morgan Kaufmann, 1993, vol. 5, pp. 164-171.
    • (1993) Advances in Neural Information Processing Systems , vol.5 , pp. 164-171
    • Hassibi, B.1    Stork, D.G.2
  • 18
    • 40949138095 scopus 로고
    • Schl. Comput. Sci, Univ. Carnegie Mellon, Pittsburgh, PA, Online, Available
    • M. Kantrowitz, "CMU artificial intelligence repository," Schl. Comput. Sci., Univ. Carnegie Mellon, Pittsburgh, PA, 1993 [Online]. Available: http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/ai-repository/ ai/areas/n% eural/bench/cmu
    • (1993) CMU artificial intelligence repository
    • Kantrowitz, M.1
  • 19
    • 0002774069 scopus 로고
    • Feature set search algorithms
    • C. H. Chen, Ed. Alphen ann den Rijn, The Netherlands: Sijthoff & Noordhoff
    • J. Kittler, "Feature set search algorithms," in Pattern Recognition and Signal Processing, C. H. Chen, Ed. Alphen ann den Rijn, The Netherlands: Sijthoff & Noordhoff, 1978, pp. 41-60.
    • (1978) Pattern Recognition and Signal Processing , pp. 41-60
    • Kittler, J.1
  • 20
    • 0036127473 scopus 로고    scopus 로고
    • Input feature selection for classification problems
    • Jan
    • N. Kwak and C. H. Choi, "Input feature selection for classification problems," IEEE Trans. Neural Netw., vol. 13, no. 1, pp. 143-159, Jan. 2002.
    • (2002) IEEE Trans. Neural Netw , vol.13 , Issue.1 , pp. 143-159
    • Kwak, N.1    Choi, C.H.2
  • 21
    • 33750123088 scopus 로고    scopus 로고
    • Feature subset selection via multi-objective genetic algorithm
    • H. C. Lac and D. A. Stacey, "Feature subset selection via multi-objective genetic algorithm," in Proc. Int. Joint Conf. Neural Netw., 2005, vol. 3, pp. 1349-1354.
    • (2005) Proc. Int. Joint Conf. Neural Netw , vol.3 , pp. 1349-1354
    • Lac, H.C.1    Stacey, D.A.2
  • 23
    • 33750124786 scopus 로고    scopus 로고
    • Feature selection using a piecewise linear network
    • Sep
    • J. Li, M. T. Manry, P. L. Narasimha, and C. Yu, "Feature selection using a piecewise linear network," IEEE Trans. Neural Netw., vol. 17, no. 5, pp. 1101-1115, Sep. 2006.
    • (2006) IEEE Trans. Neural Netw , vol.17 , Issue.5 , pp. 1101-1115
    • Li, J.1    Manry, M.T.2    Narasimha, P.L.3    Yu, C.4
  • 25
    • 35848963249 scopus 로고
    • Parsimonious network design and feature selection through node pruning
    • J. Mao, K. Mohiuddin, and A. K. Jain, "Parsimonious network design and feature selection through node pruning," in Proc. Int. Conf. Pattern Recognit., 1994, vol. 2, pp. 622-624.
    • (1994) Proc. Int. Conf. Pattern Recognit , vol.2 , pp. 622-624
    • Mao, J.1    Mohiuddin, K.2    Jain, A.K.3
  • 27
    • 0000513303 scopus 로고
    • Principled architecture selection for neural networks: Application to corporate bond rating prediction
    • San Mateo, CA: Morgan Kaufmann
    • J. Moody and J. Utans, "Principled architecture selection for neural networks: Application to corporate bond rating prediction," in Advances in Neural Information Processing Systems. San Mateo, CA: Morgan Kaufmann, 1992, vol. 4, pp. 683-690.
    • (1992) Advances in Neural Information Processing Systems , vol.4 , pp. 683-690
    • Moody, J.1    Utans, J.2
  • 28
    • 0000900876 scopus 로고
    • Skeletonization: A technique for trimming the fat from a network via relevance assessment
    • San Mateo, CA: Morgan Kaufmann
    • M. C. Mozer and P. Smolensky, "Skeletonization: A technique for trimming the fat from a network via relevance assessment," in Advances in Neural Information Processing Systems. San Mateo, CA: Morgan Kaufmann, 1989, vol. 1, pp. 107-115.
    • (1989) Advances in Neural Information Processing Systems , vol.1 , pp. 107-115
    • Mozer, M.C.1    Smolensky, P.2
  • 29
  • 30
    • 0000551189 scopus 로고    scopus 로고
    • Popular ensemble methods: An empirical study
    • D. Opitz and R. Maclin, "Popular ensemble methods: An empirical study," J. Artif. Intell. Res., vol. 11, pp. 169-198, 1999.
    • (1999) J. Artif. Intell. Res , vol.11 , pp. 169-198
    • Opitz, D.1    Maclin, R.2
  • 31
    • 0027577112 scopus 로고
    • Bayesian selection of important features for feedforward neural networks
    • K. L. Priddy, S. E. Rogers, D. W. Ruck, and G. L. Tarr, "Bayesian selection of important features for feedforward neural networks," Neurocomputing, vol. 5, no. 2-3, pp. 91-103, 1993.
    • (1993) Neurocomputing , vol.5 , Issue.2-3 , pp. 91-103
    • Priddy, K.L.1    Rogers, S.E.2    Ruck, D.W.3    Tarr, G.L.4
  • 32
    • 0028547556 scopus 로고
    • Floating search methods in feature selection
    • P. Pudil, J. Novovičová, and J. Kittler, "Floating search methods in feature selection," Pattern Recognit. Lett., vol. 15, no. 11, pp. 1119-1125, 1994.
    • (1994) Pattern Recognit. Lett , vol.15 , Issue.11 , pp. 1119-1125
    • Pudil, P.1    Novovičová, J.2    Kittler, J.3
  • 33
    • 0027662338 scopus 로고
    • Pruning algorithms - A survey
    • Sep
    • R. Reed, "Pruning algorithms - A survey," IEEE Trans. Neural Networks, vol. 4, no. 5, pp. 740-747, Sep. 1993.
    • (1993) IEEE Trans. Neural Networks , vol.4 , Issue.5 , pp. 740-747
    • Reed, R.1
  • 34
    • 0002319419 scopus 로고
    • Statistical ideas for selecting network architectures
    • B. Kappen and S. Gielen, Eds. London, U.K, Springer-Verlag
    • B. D. Ripley, "Statistical ideas for selecting network architectures," in Neural Networks: Artificial Intelligence and Industrial Applications, B. Kappen and S. Gielen, Eds. London, U.K.: Springer-Verlag, 1995, pp. 183-190.
    • (1995) Neural Networks: Artificial Intelligence and Industrial Applications , pp. 183-190
    • Ripley, B.D.1
  • 36
    • 0002429329 scopus 로고
    • Feature selection using a multilayer perceptron
    • D. W. Ruck, S. K. Rogers, and M. Kabrisky, "Feature selection using a multilayer perceptron," J. Neural Netw. Comput., vol. 2, no. 2, pp. 40-48, 1990.
    • (1990) J. Neural Netw. Comput , vol.2 , Issue.2 , pp. 40-48
    • Ruck, D.W.1    Rogers, S.K.2    Kabrisky, M.3
  • 37
    • 0031140388 scopus 로고    scopus 로고
    • Neural-network feature selector
    • May
    • R. Setiono and H. Liu, "Neural-network feature selector," IEEE Trans. Neural Netw., vol. 8, no. 3, pp. 654-662, May 1997.
    • (1997) IEEE Trans. Neural Netw , vol.8 , Issue.3 , pp. 654-662
    • Setiono, R.1    Liu, H.2
  • 39
    • 0033353812 scopus 로고    scopus 로고
    • Neural networks with periodic and monotonic activation functions: A comparative study in classification problems
    • J. M. Sopena, E. Romero, and R. Alquézar, "Neural networks with periodic and monotonic activation functions: A comparative study in classification problems," in Proc. 9th Int. Conf. Artif. Neural Netw., 1999, vol. 1, pp. 323-328.
    • (1999) Proc. 9th Int. Conf. Artif. Neural Netw , vol.1 , pp. 323-328
    • Sopena, J.M.1    Romero, E.2    Alquézar, R.3
  • 40
    • 84899002752 scopus 로고    scopus 로고
    • Fast network pruning and feature extraction using the Unit-OBS algorithm
    • Cambridge, MA: MIT Press
    • A. Stahlberger and M. Riedmiller, "Fast network pruning and feature extraction using the Unit-OBS algorithm," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 1997, vol. 9, pp. 655-661.
    • (1997) Advances in Neural Information Processing Systems , vol.9 , pp. 655-661
    • Stahlberger, A.1    Riedmiller, M.2
  • 41
    • 0030248249 scopus 로고    scopus 로고
    • Improved feature screening in feedforward neural networks
    • J. M. Steppe and K. W. Bauer, "Improved feature screening in feedforward neural networks," Neurocomputing, vol. 13, no. 1, pp. 47-58, 1996.
    • (1996) Neurocomputing , vol.13 , Issue.1 , pp. 47-58
    • Steppe, J.M.1    Bauer, K.W.2
  • 42
    • 0030190724 scopus 로고    scopus 로고
    • Integrated feature and architecture selection
    • Jul
    • J. M. Steppe, K. W. Bauer, and S. K. Rogers, "Integrated feature and architecture selection," IEEE Trans. Neural Netw., vol. 7, no. 4, pp. 1007-1013, Jul. 1996.
    • (1996) IEEE Trans. Neural Netw , vol.7 , Issue.4 , pp. 1007-1013
    • Steppe, J.M.1    Bauer, K.W.2    Rogers, S.K.3
  • 44
    • 0033069710 scopus 로고    scopus 로고
    • Partial retraining: A new approach to input relevance determination
    • P. Van de Laar, T. Heskes, and S. Gielen, "Partial retraining: A new approach to input relevance determination," Int. J. Neural Syst., vol. 9, no. 1, pp. 75-85, 1999.
    • (1999) Int. J. Neural Syst , vol.9 , Issue.1 , pp. 75-85
    • Van de Laar, P.1    Heskes, T.2    Gielen, S.3
  • 46
    • 0036721934 scopus 로고    scopus 로고
    • Feature selection with neural networks
    • A. Verikas and M. Bacauskiene, "Feature selection with neural networks," Pattern Recognit. Lett., vol. 23, no. 11, pp. 1323-1335, 2002.
    • (2002) Pattern Recognit. Lett , vol.23 , Issue.11 , pp. 1323-1335
    • Verikas, A.1    Bacauskiene, M.2
  • 47
    • 10844273163 scopus 로고    scopus 로고
    • A neural-genetic algorithm for feature selection and breast abnormality classification in digital mammography
    • P. Zhang, B. Verma, and K. Kumar, "A neural-genetic algorithm for feature selection and breast abnormality classification in digital mammography," in Proc. Int. Joint Conf. Neural Netw., 2004, vol. 3, pp. 2303-2308.
    • (2004) Proc. Int. Joint Conf. Neural Netw , vol.3 , pp. 2303-2308
    • Zhang, P.1    Verma, B.2    Kumar, K.3
  • 48
    • 0031553665 scopus 로고    scopus 로고
    • Perturbation method for deleting redundant inputs of perceptron networks
    • J. M. Zurada, A. Malinowski, and S. Usui, "Perturbation method for deleting redundant inputs of perceptron networks," Neurocomputing vol. 14, no. 2, pp. 177-193, 1997.
    • (1997) Neurocomputing , vol.14 , Issue.2 , pp. 177-193
    • Zurada, J.M.1    Malinowski, A.2    Usui, S.3


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