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Volumn 37, Issue , 2015, Pages 652-666

A new ensemble learning methodology based on hybridization of classifier ensemble selection approaches

Author keywords

Classifier combination; Classifier diversity; Dynamic ensemble selection; Ensemble learning system; Multi objective optimization; Static ensemble selection

Indexed keywords

CLASSIFICATION (OF INFORMATION); GENETIC ALGORITHMS; LEARNING SYSTEMS;

EID: 84942420474     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2015.09.009     Document Type: Article
Times cited : (66)

References (50)
  • 1
    • 33748611921 scopus 로고    scopus 로고
    • Ensemble based systems in decision making
    • R. Polikar Ensemble based systems in decision making IEEE Circuits Syst. Mag. 6 2006 21 45
    • (2006) IEEE Circuits Syst. Mag. , vol.6 , pp. 21-45
    • Polikar, R.1
  • 2
    • 79551665221 scopus 로고    scopus 로고
    • Ensemble learning with active example selection for imbalanced biomedical data classification
    • S. Oh, M.S. Lee, and B.-T. Zhang Ensemble learning with active example selection for imbalanced biomedical data classification IEEE/ACM Trans. Comput. Biol. Bioinform. 8 2011 316 325
    • (2011) IEEE/ACM Trans. Comput. Biol. Bioinform. , vol.8 , pp. 316-325
    • Oh, S.1    Lee, M.S.2    Zhang, B.-T.3
  • 3
    • 84862826692 scopus 로고    scopus 로고
    • Classifiers selection in ensembles using genetic algorithms for bankruptcy prediction
    • M.-J. Kim, and D.-K. Kang Classifiers selection in ensembles using genetic algorithms for bankruptcy prediction Expert Syst. Appl. 39 2012 9308 9314
    • (2012) Expert Syst. Appl. , vol.39 , pp. 9308-9314
    • Kim, M.-J.1    Kang, D.-K.2
  • 5
    • 35548987823 scopus 로고    scopus 로고
    • Ensemble methods for classification of patients for personalized medicine with high-dimensional data
    • H. Moon, H. Ahn, R.L. Kodell, S. Baek, C.-J. Lin, and J.J. Chen Ensemble methods for classification of patients for personalized medicine with high-dimensional data Artif. Intell. Med. 41 2007 197 207
    • (2007) Artif. Intell. Med. , vol.41 , pp. 197-207
    • Moon, H.1    Ahn, H.2    Kodell, R.L.3    Baek, S.4    Lin, C.-J.5    Chen, J.J.6
  • 6
    • 84857738059 scopus 로고    scopus 로고
    • DDD: A new ensemble approach for dealing with concept drift
    • L.L. Minku, and X. Yao DDD: a new ensemble approach for dealing with concept drift IEEE Trans. Knowl. Data Eng. 24 2012 619 633
    • (2012) IEEE Trans. Knowl. Data Eng. , vol.24 , pp. 619-633
    • Minku, L.L.1    Yao, X.2
  • 7
    • 84883308045 scopus 로고    scopus 로고
    • Incremental learning of concept drift from streaming imbalanced data
    • G. Ditzler, and R. Polikar Incremental learning of concept drift from streaming imbalanced data IEEE Trans. Knowl. Data Eng. 25 2012 2283 2301
    • (2012) IEEE Trans. Knowl. Data Eng. , vol.25 , pp. 2283-2301
    • Ditzler, G.1    Polikar, R.2
  • 8
    • 19344374461 scopus 로고    scopus 로고
    • Self-organizing information fusion and hierarchical knowledge discovery: A new framework using ARTMAP neural networks
    • G.A. Carpenter, S. Martens, and O.J. Ogas Self-organizing information fusion and hierarchical knowledge discovery: a new framework using ARTMAP neural networks Neural Netw. 18 2005 287 295
    • (2005) Neural Netw. , vol.18 , pp. 287-295
    • Carpenter, G.A.1    Martens, S.2    Ogas, O.J.3
  • 9
    • 10044270695 scopus 로고    scopus 로고
    • An evaluation of ensemble methods in handwritten word recognition based on feature selection Pattern Recognition
    • ICPR 2004, 2004
    • S. Gunter, and H. Bunke An evaluation of ensemble methods in handwritten word recognition based on feature selection Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, IEEE 2004 388 392
    • (2004) Proceedings of the 17th International Conference On, IEEE , pp. 388-392
    • Gunter, S.1    Bunke, H.2
  • 13
    • 70449654655 scopus 로고    scopus 로고
    • Clustering-and-selection model for classifier combination Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000
    • L.I. Kuncheva Clustering-and-selection model for classifier combination Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on, IEEE 2000 185 188
    • (2000) Proceedings. Fourth International Conference On, IEEE , pp. 185-188
    • Kuncheva, L.I.1
  • 14
    • 0035457787 scopus 로고    scopus 로고
    • Multiple classifiers combination by clustering and selection
    • R. Liu, and B. Yuan Multiple classifiers combination by clustering and selection Inf. Fus. 2 2001 163 168
    • (2001) Inf. Fus. , vol.2 , pp. 163-168
    • Liu, R.1    Yuan, B.2
  • 15
    • 84055219357 scopus 로고    scopus 로고
    • Classifiers selection for ensemble learning based on accuracy and diversity
    • L. Yang Classifiers selection for ensemble learning based on accuracy and diversity Procedia Eng. 15 2011 4266 4270
    • (2011) Procedia Eng. , vol.15 , pp. 4266-4270
    • Yang, L.1
  • 17
    • 84994037050 scopus 로고    scopus 로고
    • Dynamic classifier selection based on multiple classifier behaviour
    • G. Giacinto, and F. Roli Dynamic classifier selection based on multiple classifier behaviour Pattern Recognit. 34 2001 1879 1881
    • (2001) Pattern Recognit. , vol.34 , pp. 1879-1881
    • Giacinto, G.1    Roli, F.2
  • 18
    • 0036543957 scopus 로고    scopus 로고
    • Multiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection
    • P.C. Smits Multiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection IEEE Trans. Geosci. Remote Sens. 40 2002 801 813
    • (2002) IEEE Trans. Geosci. Remote Sens. , vol.40 , pp. 801-813
    • Smits, P.C.1
  • 19
    • 38349135448 scopus 로고    scopus 로고
    • From dynamic classifier selection to dynamic ensemble selection
    • A.H. Ko, R. Sabourin, and A.S. Britto Jr. From dynamic classifier selection to dynamic ensemble selection Pattern Recognit. 41 2008 1718 1731
    • (2008) Pattern Recognit. , vol.41 , pp. 1718-1731
    • Ko, A.H.1    Sabourin, R.2    Britto, A.S.3
  • 20
    • 84858073995 scopus 로고    scopus 로고
    • A measure of competence based on random classification for dynamic ensemble selection
    • T. Woloszynski, M. Kurzynski, P. Podsiadlo, and G.W. Stachowiak A measure of competence based on random classification for dynamic ensemble selection Inf. Fus. 13 2012 207 213
    • (2012) Inf. Fus. , vol.13 , pp. 207-213
    • Woloszynski, T.1    Kurzynski, M.2    Podsiadlo, P.3    Stachowiak, G.W.4
  • 21
    • 84951870511 scopus 로고    scopus 로고
    • On a new measure of classifier competence in the feature space
    • Springer
    • T. Woloszynski, and M. Kurzynski On a new measure of classifier competence in the feature space Computer Recognition Systems 3 2009 Springer 285 292
    • (2009) Computer Recognition Systems 3 , pp. 285-292
    • Woloszynski, T.1    Kurzynski, M.2
  • 22
    • 70649104621 scopus 로고    scopus 로고
    • Dynamic classifier ensemble selection based on GMDH Computational Sciences and Optimization, 2009
    • CSO 2009
    • J. Xiao, and C. He Dynamic classifier ensemble selection based on GMDH Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on, IEEE 2009 731 734
    • (2009) International Joint Conference On, IEEE 2009 , pp. 731-734
    • Xiao, J.1    He, C.2
  • 23
    • 79958833224 scopus 로고    scopus 로고
    • A probabilistic model of classifier competence for dynamic ensemble selection
    • T. Woloszynski, and M. Kurzynski A probabilistic model of classifier competence for dynamic ensemble selection Pattern Recognit. 44 2011 2656 2668
    • (2011) Pattern Recognit. , vol.44 , pp. 2656-2668
    • Woloszynski, T.1    Kurzynski, M.2
  • 25
    • 10444224737 scopus 로고    scopus 로고
    • Classifier selection for majority voting
    • D. Ruta, and B. Gabrys Classifier selection for majority voting Inf. Fus. 6 2005 63 81
    • (2005) Inf. Fus. , vol.6 , pp. 63-81
    • Ruta, D.1    Gabrys, B.2
  • 26
    • 77958150674 scopus 로고    scopus 로고
    • A dynamic classifier ensemble selection approach for noise data
    • J. Xiao, C. He, X. Jiang, and D. Liu A dynamic classifier ensemble selection approach for noise data Inf. Sci. 180 2010 3402 3421
    • (2010) Inf. Sci. , vol.180 , pp. 3402-3421
    • Xiao, J.1    He, C.2    Jiang, X.3    Liu, D.4
  • 27
    • 33845291177 scopus 로고    scopus 로고
    • Trade-off between diversity and accuracy in ensemble generation
    • Springer
    • A. Chandra, H. Chen, and X. Yao Trade-off between diversity and accuracy in ensemble generation Multi-objective Machine Learning 2006 Springer 429 464
    • (2006) Multi-objective Machine Learning , pp. 429-464
    • Chandra, A.1    Chen, H.2    Yao, X.3
  • 30
    • 0345879981 scopus 로고    scopus 로고
    • A novel multicriteria optimization algorithm for the structure determination of multilayer feedforward neural networks
    • K. Kottathra, and Y. Attikiouzel A novel multicriteria optimization algorithm for the structure determination of multilayer feedforward neural networks J. Netw. Comput. Appl. 19 1996 135 147
    • (1996) J. Netw. Comput. Appl. , vol.19 , pp. 135-147
    • Kottathra, K.1    Attikiouzel, Y.2
  • 31
    • 0033169217 scopus 로고    scopus 로고
    • Multiobjective genetic optimization of diagnostic classifiers with implications for generating receiver operating characteristic curves
    • M.A. Kupinski, and M.A. Anastasio Multiobjective genetic optimization of diagnostic classifiers with implications for generating receiver operating characteristic curves IEEE Trans. Med. Imaging 18 1999 675 685
    • (1999) IEEE Trans. Med. Imaging , vol.18 , pp. 675-685
    • Kupinski, M.A.1    Anastasio, M.A.2
  • 32
    • 0141542628 scopus 로고    scopus 로고
    • Speeding up backpropagation using multiobjective evolutionary algorithms
    • H.A. Abbass Speeding up backpropagation using multiobjective evolutionary algorithms Neural Comput. 15 2003 2705 2726
    • (2003) Neural Comput. , vol.15 , pp. 2705-2726
    • Abbass, H.A.1
  • 33
    • 32544450795 scopus 로고    scopus 로고
    • Ensemble learning using multi-objective evolutionary algorithms
    • A. Chandra, and X. Yao Ensemble learning using multi-objective evolutionary algorithms J. Math. Model. Algorithms 5 2006 417 445
    • (2006) J. Math. Model. Algorithms , vol.5 , pp. 417-445
    • Chandra, A.1    Yao, X.2
  • 35
    • 78149249078 scopus 로고    scopus 로고
    • Multiobjective neural network ensembles based on regularized negative correlation learning
    • H. Chen, and X. Yao Multiobjective neural network ensembles based on regularized negative correlation learning IEEE Trans. Knowl. Data Eng. 22 2010 1738 1751
    • (2010) IEEE Trans. Knowl. Data Eng. , vol.22 , pp. 1738-1751
    • Chen, H.1    Yao, X.2
  • 36
    • 84870058393 scopus 로고    scopus 로고
    • Black hole: A new heuristic optimization approach for data clustering
    • A. Hatamlou Black hole: a new heuristic optimization approach for data clustering Inf. Sci. 222 2013 175 184
    • (2013) Inf. Sci. , vol.222 , pp. 175-184
    • Hatamlou, A.1
  • 37
    • 84926431460 scopus 로고    scopus 로고
    • Ions motion algorithm for solving optimization problems
    • B. Javidy, A. Hatamlou, and S. Mirjalili Ions motion algorithm for solving optimization problems Appl. Soft Comput. 32 2015 72 79
    • (2015) Appl. Soft Comput. , vol.32 , pp. 72-79
    • Javidy, B.1    Hatamlou, A.2    Mirjalili, S.3
  • 40
    • 10444221886 scopus 로고    scopus 로고
    • Diversity creation methods: A survey and categorisation
    • G. Brown, J. Wyatt, R. Harris, and X. Yao Diversity creation methods: a survey and categorisation Inf. Fus. 6 2005 5 20
    • (2005) Inf. Fus. , vol.6 , pp. 5-20
    • Brown, G.1    Wyatt, J.2    Harris, R.3    Yao, X.4
  • 41
    • 80052896776 scopus 로고    scopus 로고
    • An introduction to the bootstrap
    • R.W. Johnson An introduction to the bootstrap Teach. Stat. 23 2001 49 54
    • (2001) Teach. Stat. , vol.23 , pp. 49-54
    • Johnson, R.W.1
  • 42
    • 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. Pattern Anal. Mach. Intell. 20 1998 832 844
    • (1998) IEEE Trans. Pattern Anal. Mach. Intell. , vol.20 , pp. 832-844
    • Ho, T.K.1
  • 43
    • 0037403516 scopus 로고    scopus 로고
    • Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
    • L.I. Kuncheva, and C.J. Whitaker Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy Mach. Learn. 51 2003 181 207
    • (2003) Mach. Learn. , vol.51 , pp. 181-207
    • Kuncheva, L.I.1    Whitaker, C.J.2
  • 45
    • 0035420134 scopus 로고    scopus 로고
    • Design of effective neural network ensembles for image classification purposes
    • G. Giacinto, and F. Roli Design of effective neural network ensembles for image classification purposes Image Vis. Comput. 19 2001 699 707
    • (2001) Image Vis. Comput. , vol.19 , pp. 699-707
    • Giacinto, G.1    Roli, F.2
  • 47
    • 84875272868 scopus 로고    scopus 로고
    • Ensemble classifier generation using non-uniform layered clustering and Genetic Algorithm
    • A. Rahman, and B. Verma Ensemble classifier generation using non-uniform layered clustering and Genetic Algorithm Knowl. Based Syst. 43 2013 30 42
    • (2013) Knowl. Based Syst. , vol.43 , pp. 30-42
    • Rahman, A.1    Verma, B.2
  • 49
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • J. Demšar Statistical comparisons of classifiers over multiple data sets J. Mach. Learn. Res. 7 2006 1 30
    • (2006) J. Mach. Learn. Res. , vol.7 , pp. 1-30
    • Demšar, J.1
  • 50
    • 35248833039 scopus 로고    scopus 로고
    • No free lunch and free leftovers theorems for multiobjective optimisation problems
    • Springer
    • D.W. Corne, and J.D. Knowles No free lunch and free leftovers theorems for multiobjective optimisation problems Evolutionary Multi-Criterion Optimization 2003 Springer 327 341
    • (2003) Evolutionary Multi-Criterion Optimization , pp. 327-341
    • Corne, D.W.1    Knowles, J.D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.