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Volumn 43, Issue 6, 2010, Pages 2082-2105

IFS-CoCo: Instance and feature selection based on cooperative coevolution with nearest neighbor rule

Author keywords

Cooperative coevolution; Evolutionary algorithms; Feature selection; Instance selection; Nearest neighbor

Indexed keywords

CLASSIFICATION TASKS; COMPUTATIONAL PROBLEM; COOPERATIVE COEVOLUTION; EVOLUTIONARY APPROACH; EVOLUTIONARY MODELS; FEATURE SELECTION; INSTANCE SELECTION; NEAREST NEIGHBOR CLASSIFICATION; NEAREST NEIGHBOR RULE; NEAREST NEIGHBORS; NON-PARAMETRIC STATISTICAL TESTS;

EID: 76749096459     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2009.12.012     Document Type: Article
Times cited : (84)

References (92)
  • 2
    • 84944611438 scopus 로고    scopus 로고
    • X. Wu, V. Kumar Eds, Chapman & Hall, CRC, London, Boca Raton
    • X. Wu, V. Kumar (Eds.), The Top Ten Algorithms in Data Mining, Chapman & Hall, CRC, London, Boca Raton, 2009.
    • (2009) The Top Ten Algorithms in Data Mining
  • 4
    • 33750719625 scopus 로고    scopus 로고
    • Efficient data reduction in multimedia data
    • Wang-Manoranjan S., and Xu D.C. Efficient data reduction in multimedia data. Applied Intelligence 25 (2006) 359-374
    • (2006) Applied Intelligence , vol.25 , pp. 359-374
    • Wang-Manoranjan, S.1    Xu, D.C.2
  • 5
    • 10044273895 scopus 로고    scopus 로고
    • Data reduction of large vector graphics
    • Kolesnikov A., and Frantib P. Data reduction of large vector graphics. Pattern Recognition 38 (2005) 381-394
    • (2005) Pattern Recognition , vol.38 , pp. 381-394
    • Kolesnikov, A.1    Frantib, P.2
  • 6
    • 34250851661 scopus 로고    scopus 로고
    • On using prototype reduction schemes to optimize dissimilarity-based classification
    • Kim S.W., and Oomenn B.J. On using prototype reduction schemes to optimize dissimilarity-based classification. Pattern Recognition 40 11 (2007) 2946-2957
    • (2007) Pattern Recognition , vol.40 , Issue.11 , pp. 2946-2957
    • Kim, S.W.1    Oomenn, B.J.2
  • 7
    • 53949099623 scopus 로고    scopus 로고
    • Subgroup discovery in large size data sets preprocessed using stratified instance selection for increasing the presence of minority classes
    • Cano J.R., García S., and Herrera F. Subgroup discovery in large size data sets preprocessed using stratified instance selection for increasing the presence of minority classes. Pattern Recognition Letters 29 (2008) 2156-2164
    • (2008) Pattern Recognition Letters , vol.29 , pp. 2156-2164
    • Cano, J.R.1    García, S.2    Herrera, F.3
  • 8
    • 67649404578 scopus 로고    scopus 로고
    • On using prototype reduction schemes to enhance the computation of volume-based inter-class overlap measures
    • S.W. Kim, B.J. Oomenn, On using prototype reduction schemes to enhance the computation of volume-based inter-class overlap measures, Pattern Recognition 42(11) (2009) 2695-2704.
    • (2009) Pattern Recognition , vol.42 , Issue.11 , pp. 2695-2704
    • Kim, S.W.1    Oomenn, B.J.2
  • 10
    • 43449084041 scopus 로고    scopus 로고
    • Prototype-based classification
    • Perner P. Prototype-based classification. Applied Intelligence 28 (2008) 238-246
    • (2008) Applied Intelligence , vol.28 , pp. 238-246
    • Perner, P.1
  • 12
    • 85130883648 scopus 로고    scopus 로고
    • H. Liu, H. Motoda Eds, Chapman & Hall, CRC, London, Boca Raton
    • H. Liu, H. Motoda (Eds.), Computational Methods of Feature Selection, Chapman & Hall, CRC, London, Boca Raton, 2007.
    • (2007) Computational Methods of Feature Selection
  • 13
    • 0347763609 scopus 로고    scopus 로고
    • Using evolutionary algorithms as instance selection for data reduction in KDD: An experimental study
    • Cano J.R., Herrera F., and Lozano M. Using evolutionary algorithms as instance selection for data reduction in KDD: An experimental study. IEEE Transactions on Evolutionary Computation 7 (2003) 561-575
    • (2003) IEEE Transactions on Evolutionary Computation , vol.7 , pp. 561-575
    • Cano, J.R.1    Herrera, F.2    Lozano, M.3
  • 15
    • 42749092345 scopus 로고    scopus 로고
    • A memetic algorithm for evolutionary prototype selection: a scaling up approach
    • García S., Cano J.R., and Herrera F. A memetic algorithm for evolutionary prototype selection: a scaling up approach. Pattern Recognition 41 8 (2008) 2693-2709
    • (2008) Pattern Recognition , vol.41 , Issue.8 , pp. 2693-2709
    • García, S.1    Cano, J.R.2    Herrera, F.3
  • 16
    • 0000935031 scopus 로고
    • Editing for the k-nearest neighbors rule by a genetic algorithm
    • Kuncheva L.I. Editing for the k-nearest neighbors rule by a genetic algorithm. Pattern Recognition Letters 16 (1995) 809-814
    • (1995) Pattern Recognition Letters , vol.16 , pp. 809-814
    • Kuncheva, L.I.1
  • 17
    • 0343773003 scopus 로고    scopus 로고
    • Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms
    • Inza I., Larraaga P., and Sierra B. Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms. International Journal of Approximate Reasoning 27 (2001) 143-164
    • (2001) International Journal of Approximate Reasoning , vol.27 , pp. 143-164
    • Inza, I.1    Larraaga, P.2    Sierra, B.3
  • 22
    • 36749021623 scopus 로고    scopus 로고
    • A genetic approach for efficient outlier detection in projected space
    • Bandyopadhyay S., and Santanu S. A genetic approach for efficient outlier detection in projected space. Pattern Recognition 41 (2008) 1338-1349
    • (2008) Pattern Recognition , vol.41 , pp. 1338-1349
    • Bandyopadhyay, S.1    Santanu, S.2
  • 23
    • 0034153728 scopus 로고    scopus 로고
    • Cooperative coevolution: an architecture for evolving coadapted subcomponents
    • Potter M.A., and De Jong K.A. Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evolutionary Computation 8 (2000) 1-29
    • (2000) Evolutionary Computation , vol.8 , pp. 1-29
    • Potter, M.A.1    De Jong, K.A.2
  • 25
    • 33749251505 scopus 로고    scopus 로고
    • A cooperative constructive method for neural networks for pattern recognition
    • Garcia-Pedrajas N., and Ortiz-Boyer D. A cooperative constructive method for neural networks for pattern recognition. Pattern Recognition 40 1 (2007) 80-98
    • (2007) Pattern Recognition , vol.40 , Issue.1 , pp. 80-98
    • Garcia-Pedrajas, N.1    Ortiz-Boyer, D.2
  • 26
    • 10444261979 scopus 로고    scopus 로고
    • The cooperative coevolutionary (1 + 1) EA
    • Wiegand R.P., and Jansen T. The cooperative coevolutionary (1 + 1) EA. Evolutionary Computation 12 (2004) 405-434
    • (2004) Evolutionary Computation , vol.12 , pp. 405-434
    • Wiegand, R.P.1    Jansen, T.2
  • 27
    • 0001884644 scopus 로고
    • Individual comparisons by rankings methods
    • Wilcoxon F. Individual comparisons by rankings methods. Biometrics 1 (1945) 80-83
    • (1945) Biometrics , vol.1 , pp. 80-83
    • Wilcoxon, F.1
  • 29
    • 33845982223 scopus 로고    scopus 로고
    • Evolutionary stratified training set selection for extracting classification rules with trade-off precision-interpretability
    • Cano J.R., Herrera F., and Lozano M. Evolutionary stratified training set selection for extracting classification rules with trade-off precision-interpretability. Data and Knowledge Engineering 60 (2007) 90-100
    • (2007) Data and Knowledge Engineering , vol.60 , pp. 90-100
    • Cano, J.R.1    Herrera, F.2    Lozano, M.3
  • 30
    • 30944444787 scopus 로고    scopus 로고
    • Artificial neural networks with evolutionary instance selection for financial forecasting
    • Kim K. Artificial neural networks with evolutionary instance selection for financial forecasting. Expert Systems with Applications 30 (2006) 519-526
    • (2006) Expert Systems with Applications , vol.30 , pp. 519-526
    • Kim, K.1
  • 31
    • 0343081513 scopus 로고    scopus 로고
    • Reduction techniques for instance-based learning algorithms
    • Wilson D.R., and Martinez T.R. Reduction techniques for instance-based learning algorithms. Machine Learning 38 (2000) 257-286
    • (2000) Machine Learning , vol.38 , pp. 257-286
    • Wilson, D.R.1    Martinez, T.R.2
  • 33
    • 0015361129 scopus 로고
    • Asymptotic properties of nearest neighbor rules using edited data
    • Wilson D.L. Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man and Cybernetics 3 (1972) 408-421
    • (1972) IEEE Transactions on Systems, Man and Cybernetics , vol.3 , pp. 408-421
    • Wilson, D.L.1
  • 34
    • 46249122128 scopus 로고    scopus 로고
    • Hit miss networks with applications to instance selection
    • Marchiori E. Hit miss networks with applications to instance selection. Journal of Machine Learning Research 9 (2008) 997-1017
    • (2008) Journal of Machine Learning Research , vol.9 , pp. 997-1017
    • Marchiori, E.1
  • 37
    • 9444236865 scopus 로고    scopus 로고
    • Comparison of instances selection algorithms I
    • Algorithms survey, Springer, Berlin
    • N. Jankowski, M. Grochowski, Comparison of instances selection algorithms I. Algorithms survey, in: Lecture Notes in Computer Science, vol. 3070, Springer, Berlin, 2004, pp. 598-603.
    • (2004) Lecture Notes in Computer Science , vol.3070 , pp. 598-603
    • Jankowski, N.1    Grochowski, M.2
  • 38
    • 0346331906 scopus 로고    scopus 로고
    • A brief taxonomy and ranking of creative prototype reduction schemes
    • Kim S.W., and Oomenn B.J. A brief taxonomy and ranking of creative prototype reduction schemes. Pattern Analysis and Applications 6 (2003) 232-244
    • (2003) Pattern Analysis and Applications , vol.6 , pp. 232-244
    • Kim, S.W.1    Oomenn, B.J.2
  • 39
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature selection
    • Kohavi R., and John G. Wrappers for feature selection. Artificial Intelligence 97 (1997) 273-324
    • (1997) Artificial Intelligence , vol.97 , pp. 273-324
    • Kohavi, R.1    John, G.2
  • 41
    • 35748932917 scopus 로고    scopus 로고
    • A review of feature selection techniques in bioinformatics
    • Saeys Y., Inza I., and Larranaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 19 (2007) 2507-2517
    • (2007) Bioinformatics , vol.19 , pp. 2507-2517
    • Saeys, Y.1    Inza, I.2    Larranaga, P.3
  • 43
    • 67349250195 scopus 로고    scopus 로고
    • Feature selection based on loss-margin of nearest neighbor classification
    • Li Y., and Lu B.L. Feature selection based on loss-margin of nearest neighbor classification. Pattern Recognition 42 9 (2009) 1914-1921
    • (2009) Pattern Recognition , vol.42 , Issue.9 , pp. 1914-1921
    • Li, Y.1    Lu, B.L.2
  • 45
    • 37649019425 scopus 로고    scopus 로고
    • Feature subset selection based on fuzzy entropy measures for handling classification problems
    • Shie J., and Chen S. Feature subset selection based on fuzzy entropy measures for handling classification problems. Applied Intelligence 28 (2008) 69-82
    • (2008) Applied Intelligence , vol.28 , pp. 69-82
    • Shie, J.1    Chen, S.2
  • 46
    • 17044405923 scopus 로고    scopus 로고
    • Toward integrating feature selection algorithms for classification and clustering
    • Liu H., and Yu L. Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering 17 3 (2005) 1-12
    • (2005) IEEE Transactions on Knowledge and Data Engineering , vol.17 , Issue.3 , pp. 1-12
    • Liu, H.1    Yu, L.2
  • 47
    • 0033220766 scopus 로고    scopus 로고
    • Nearest neighbor classifier: simultaneous editing and descriptor selection
    • Kuncheva L.I., and Jain L.C. Nearest neighbor classifier: simultaneous editing and descriptor selection. Pattern Recognition Letters 20 (1999) 1149-1156
    • (1999) Pattern Recognition Letters , vol.20 , pp. 1149-1156
    • Kuncheva, L.I.1    Jain, L.C.2
  • 50
    • 26444479778 scopus 로고
    • Optimization by simulated annealing
    • Kirkpatrick S., Gelatt C.D., and Vecchi M.P. Optimization by simulated annealing. Science 4598 (1983) 671-680
    • (1983) Science , vol.4598 , pp. 671-680
    • Kirkpatrick, S.1    Gelatt, C.D.2    Vecchi, M.P.3
  • 51
    • 58549117670 scopus 로고    scopus 로고
    • Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach
    • Ahn H., and Kim K. Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach. Applied Soft Computing 9 (2009) 599-607
    • (2009) Applied Soft Computing , vol.9 , pp. 599-607
    • Ahn, H.1    Kim, K.2
  • 52
    • 0001334115 scopus 로고
    • The CHC adaptative search algorithm: how to have safe search when engaging in nontraditional genetic recombination
    • Rawlins G.J.E. (Ed)
    • Eshelman L.J. The CHC adaptative search algorithm: how to have safe search when engaging in nontraditional genetic recombination. In: Rawlins G.J.E. (Ed). Foundations of Genetic Algorithms (1991) 265-283
    • (1991) Foundations of Genetic Algorithms , pp. 265-283
    • Eshelman, L.J.1
  • 54
    • 84956859473 scopus 로고    scopus 로고
    • Evolution of reference sets in nearest neighbor classification
    • Ishibuchi H., and Nakashima T. Evolution of reference sets in nearest neighbor classification. Lecture Notes in Computer Science vol. 1585 (1999) 82-89
    • (1999) Lecture Notes in Computer Science , vol.1585 , pp. 82-89
    • Ishibuchi, H.1    Nakashima, T.2
  • 55
    • 23044528412 scopus 로고    scopus 로고
    • Prototype selection and feature subset selection by estimation of distribution algorithms
    • A case study in the survival of cirrhotic patients treated with TIPS, Springer, Berlin
    • B. Sierra, E. Lazkano, I. Inza, M. Merino, P. Larraaga, J. Quiroga, Prototype selection and feature subset selection by estimation of distribution algorithms. A case study in the survival of cirrhotic patients treated with TIPS, in: Lecture Notes in Artificial Intelligence, vol. 2101, Springer, Berlin, 2001, pp. 20-29.
    • (2001) Lecture Notes in Artificial Intelligence , vol.2101 , pp. 20-29
    • Sierra, B.1    Lazkano, E.2    Inza, I.3    Merino, M.4    Larraaga, P.5    Quiroga, J.6
  • 56
    • 0000751098 scopus 로고    scopus 로고
    • Using learning to facilitate the evolution of features for recognizing visual concepts
    • Bala J., De Jong K.A., Huang J., Vafaie H., and Wechsler H. Using learning to facilitate the evolution of features for recognizing visual concepts. Evolutionary Computation 4 3 (1997) 297-311
    • (1997) Evolutionary Computation , vol.4 , Issue.3 , pp. 297-311
    • Bala, J.1    De Jong, K.A.2    Huang, J.3    Vafaie, H.4    Wechsler, H.5
  • 57
    • 0035426683 scopus 로고    scopus 로고
    • Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems
    • Casillas J., Cordon O., Del Jesus M.J., and Herrera F. Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems. Information Sciences 136 (2001) 135-157
    • (2001) Information Sciences , vol.136 , pp. 135-157
    • Casillas, J.1    Cordon, O.2    Del Jesus, M.J.3    Herrera, F.4
  • 58
    • 0035359278 scopus 로고    scopus 로고
    • Selection of relevant features in a fuzzy genetic learning algorithm
    • Gonzalez A., and Perez R. Selection of relevant features in a fuzzy genetic learning algorithm. IEEE Transactions on Systems, Man and Cybernetics 31 3 (2001) 417-425
    • (2001) IEEE Transactions on Systems, Man and Cybernetics , vol.31 , Issue.3 , pp. 417-425
    • Gonzalez, A.1    Perez, R.2
  • 59
    • 38349121661 scopus 로고    scopus 로고
    • Genetic algorithm-based feature set partitioning for classification problems
    • Rokach L. Genetic algorithm-based feature set partitioning for classification problems. Pattern Recognition 41 (2008) 1676-1700
    • (2008) Pattern Recognition , vol.41 , pp. 1676-1700
    • Rokach, L.1
  • 60
    • 0024895461 scopus 로고
    • A note on genetic algorithm for large-scale feature selection
    • Siedlecki W., and Sklansky J. A note on genetic algorithm for large-scale feature selection. Pattern Recognition Letters 10 (1989) 335-347
    • (1989) Pattern Recognition Letters , vol.10 , pp. 335-347
    • Siedlecki, W.1    Sklansky, J.2
  • 61
    • 58349092287 scopus 로고    scopus 로고
    • Evolutionary-based feature selection approaches with new criteria for data mining: a case study of credit approval data
    • Wang C., and Huang Y. Evolutionary-based feature selection approaches with new criteria for data mining: a case study of credit approval data. Expert Systems with Applications 36 (2009) 5900-5908
    • (2009) Expert Systems with Applications , vol.36 , pp. 5900-5908
    • Wang, C.1    Huang, Y.2
  • 62
    • 17444397485 scopus 로고    scopus 로고
    • Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection
    • Zhang P., Verma B., and Kumar K. Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection. Pattern Recognition Letters 26 7 (2005) 909-919
    • (2005) Pattern Recognition Letters , vol.26 , Issue.7 , pp. 909-919
    • Zhang, P.1    Verma, B.2    Kumar, K.3
  • 63
    • 0036832996 scopus 로고    scopus 로고
    • Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm
    • Ho S., Liu C., and Liu S. Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm. Pattern Recognition Letters 23 (2002) 1495-1503
    • (2002) Pattern Recognition Letters , vol.23 , pp. 1495-1503
    • Ho, S.1    Liu, C.2    Liu, S.3
  • 67
  • 71
    • 34547241457 scopus 로고    scopus 로고
    • Sequential versus parallel cooperative coevolutionary algorithms for optimization
    • Vancouver
    • E. Popovici, K.A. De Jong, Sequential versus parallel cooperative coevolutionary algorithms for optimization, IEEE Congress on Evolutionary Computation, Vancouver, 2006, pp. 1610-1617.
    • (2006) IEEE Congress on Evolutionary Computation , pp. 1610-1617
    • Popovici, E.1    De Jong, K.A.2
  • 76
    • 76749131627 scopus 로고    scopus 로고
    • UCI repository of machine learning databases, URL: 〈
    • A. Asuncion, D.J. Newman, UCI repository of machine learning databases, 2007, URL: 〈 http://www.ics.uci.edu/∼mlearn/MLRepository.html〉.
    • (2007)
    • Asuncion, A.1    Newman, D.J.2
  • 78
    • 0034274591 scopus 로고    scopus 로고
    • A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms
    • Lim T.S., Loh W.Y., and Shih Y.S. A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning 40 3 (2000) 203-228
    • (2000) Machine Learning , vol.40 , Issue.3 , pp. 203-228
    • Lim, T.S.1    Loh, W.Y.2    Shih, Y.S.3
  • 79
    • 84973587732 scopus 로고
    • A coefficient of agreement for nominal scales
    • Cohen J. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20 1 (1960) 37-46
    • (1960) Educational and Psychological Measurement , vol.20 , Issue.1 , pp. 37-46
    • Cohen, J.1
  • 81
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • Demšar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7 (2006) 1-30
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1-30
    • Demšar, J.1
  • 82
    • 58149287952 scopus 로고    scopus 로고
    • An extension on "Statistical comparisons of classifiers over multiple data sets" for all pairwise comparisons
    • García S., and Herrera F. An extension on "Statistical comparisons of classifiers over multiple data sets" for all pairwise comparisons. Journal of Machine Learning Research 9 (2008) 2677-2694
    • (2008) Journal of Machine Learning Research , vol.9 , pp. 2677-2694
    • García, S.1    Herrera, F.2
  • 84
    • 0004252445 scopus 로고    scopus 로고
    • Prentice-Hall, Englewood Cliffs, London
    • Zar J.H. Biostatistical Analysis (1999), Prentice-Hall, Englewood Cliffs, London
    • (1999) Biostatistical Analysis
    • Zar, J.H.1
  • 85
    • 0036104537 scopus 로고    scopus 로고
    • Advances in instance selection for instance-based learning algorithms
    • Brighton H., and Mellish C. Advances in instance selection for instance-based learning algorithms. Data Mining and Knowledge Discovery 6 2 (2002) 153-172
    • (2002) Data Mining and Knowledge Discovery , vol.6 , Issue.2 , pp. 153-172
    • Brighton, H.1    Mellish, C.2
  • 90
    • 17444379003 scopus 로고    scopus 로고
    • Stratification for scaling up evolutionary prototype selection
    • Cano J.R., Herrera F., and Lozano M. Stratification for scaling up evolutionary prototype selection. Pattern Recognition Letters 26 (2005) 953-963
    • (2005) Pattern Recognition Letters , vol.26 , pp. 953-963
    • Cano, J.R.1    Herrera, F.2    Lozano, M.3
  • 91
    • 65049087517 scopus 로고    scopus 로고
    • A divide-and-conquer recursive approach for scaling up instance selection algorithms
    • Haro-García A., and García-Pedrajas N. A divide-and-conquer recursive approach for scaling up instance selection algorithms. Data Mining and Knowledge Discovery 18 (2009) 392-418
    • (2009) Data Mining and Knowledge Discovery , vol.18 , pp. 392-418
    • Haro-García, A.1    García-Pedrajas, N.2


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