메뉴 건너뛰기




Volumn 39, Issue 18, 2012, Pages 13253-13266

GRADIENT: Grammar-driven genetic programming framework for building multi-component, hierarchical predictive systems

Author keywords

Ensemble systems; Function approximation; Grammar driven genetic programming; Multi level prediction systems; Non linear regression

Indexed keywords

AUTOMATIC BUILDINGS; COMPONENT MODEL; ENSEMBLE SYSTEMS; FUNCTION APPROXIMATION; HIERARCHICAL STRUCTURES; MULTI POPULATION; MULTICOMPONENTS; NON-LINEAR REGRESSION; PREDICTION METHODS; PREDICTION SYSTEMS; PREDICTIVE SYSTEMS; PROGRAMMING FRAMEWORK; RESAMPLING;

EID: 84865229886     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.05.076     Document Type: Article
Times cited : (25)

References (61)
  • 1
    • 0002265406 scopus 로고    scopus 로고
    • Parallel genetic programming: A scalable implementation using the transputer network
    • MIT Press Cambridge USA
    • D. André, and J.R. Koza Parallel genetic programming: A scalable implementation using the transputer network Advances in genetic programming 1996 MIT Press Cambridge USA (pp. 317-337)
    • (1996) Advances in Genetic Programming
    • André, D.1    Koza, J.R.2
  • 6
    • 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 Information Fusion 6 2005 520 (Special issue on Diversity in multiple classifier systems)
    • (2005) Information Fusion , vol.6 , pp. 520
    • Brown, G.1    Wyatt, J.2    Harris, R.3    Yao, X.4
  • 7
    • 78650293821 scopus 로고    scopus 로고
    • Ridge regression ensemble for toxicity prediction
    • M. Budka, and B. Gabrys Ridge regression ensemble for toxicity prediction Procedia Computer Science 1 1 2010 193 201
    • (2010) Procedia Computer Science , vol.1 , Issue.1 , pp. 193-201
    • Budka, M.1    Gabrys, B.2
  • 8
    • 77952506944 scopus 로고    scopus 로고
    • Robust predictive modelling of water pollution using biomarker data
    • M. Budka, B. Gabrys, and E. Ravagnan Robust predictive modelling of water pollution using biomarker data Water Research 44 10 2010 3294 3308
    • (2010) Water Research , vol.44 , Issue.10 , pp. 3294-3308
    • Budka, M.1    Gabrys, B.2    Ravagnan, E.3
  • 9
    • 32544431928 scopus 로고    scopus 로고
    • Evolving hybrid ensembles of learning machines for better generalisation
    • DOI 10.1016/j.neucom.2005.12.014, PII S0925231205003188
    • A. Chandra, and X. Yao Evolving hybrid ensembles of learning machines for better generalisation Neurocomputing 69 2006 686 700 (Pubitemid 43230374)
    • (2006) Neurocomputing , vol.69 , Issue.7-9 SPEC. ISS. , pp. 686-700
    • Chandra, A.1    Yao, X.2
  • 10
    • 45249128876 scopus 로고
    • Combining forecasts: A review and annotated bibliography
    • R.T. Clemen Combining forecasts: A review and annotated bibliography International Journal of Forecasting 5 4 1989 559 583
    • (1989) International Journal of Forecasting , vol.5 , Issue.4 , pp. 559-583
    • Clemen, R.T.1
  • 11
    • 80052940712 scopus 로고    scopus 로고
    • Multi-objective design of hierarchical consensus functions for clustering ensembles via genetic programming
    • A. Coelho, E. Fernandes, and K. Faceli Multi-objective design of hierarchical consensus functions for clustering ensembles via genetic programming Decision Support Systems 51 4 2011 794 809
    • (2011) Decision Support Systems , vol.51 , Issue.4 , pp. 794-809
    • Coelho, A.1    Fernandes, E.2    Faceli, K.3
  • 12
    • 0000913324 scopus 로고    scopus 로고
    • SVMTorch: Support Vector Machines for large-scale regression problems
    • R. Colobert, and S. Bengio SVMTorch: Support vector machines for large-scale regression problems Journal of Machine Learning Research 1 2001 143 160 (Pubitemid 33738779)
    • (2001) Journal of Machine Learning Research , vol.1 , Issue.2 , pp. 143-160
    • Collobert, R.1    Bengio, S.2
  • 13
    • 0028416938 scopus 로고
    • Independent component analysis: A new concept?
    • Elsevier
    • P. Comon Independent component analysis: A new concept? Signal Processing 36 1994 Elsevier 3 (pp. 287-314)
    • (1994) Signal Processing , vol.36
    • Comon, P.1
  • 14
    • 80053403826 scopus 로고    scopus 로고
    • Ensemble methods in machine learning
    • T. Dietterich Ensemble methods in machine learning Multiple Classifier Systems 1857 2000 115
    • (2000) Multiple Classifier Systems , vol.1857 , pp. 115
    • Dietterich, T.1
  • 17
    • 0002344794 scopus 로고
    • Bootstrap methods: Another look at the jackknife
    • B. Efron Bootstrap methods: Another look at the jackknife The Annals of Statistics 7 1 1979 126
    • (1979) The Annals of Statistics , vol.7 , Issue.1 , pp. 126
    • Efron, B.1
  • 18
    • 0023331453 scopus 로고
    • Attributes of the performance of central processing units: A relative performance prediction model
    • DOI 10.1145/32232.32234
    • P. Ein-Dor, and J. Feldmesser Attributes of the performance of central processing units: A relative performance prediction model Communications of the ACM 30 4 1984 308 317 (Pubitemid 17550640)
    • (1987) Communications of the ACM , vol.30 , Issue.4 , pp. 308-317
    • Ein-Dor Philip1    Feldmesser Jacob2
  • 19
    • 2342622786 scopus 로고    scopus 로고
    • Leave one out error, stability, and generalization of voting combinations of classifiers
    • T. Evgeniou, M. Pontil, and A. Elisseeff Leave one out error, stability, and generalization of voting combinations of classifiers Machine Learning 55 1 2004 71 97
    • (2004) Machine Learning , vol.55 , Issue.1 , pp. 71-97
    • Evgeniou, T.1    Pontil, M.2    Elisseeff, A.3
  • 23
    • 78649934709 scopus 로고    scopus 로고
    • Irvine, CA: University of California, School of Information and Computer Science
    • Frank, A.; & Asuncion, A. (2010). UCI machine learning repository. Irvine, CA: University of California, School of Information and Computer Science. .
    • (2010) UCI Machine Learning Repository
    • Frank, A.1    Asuncion, A.2
  • 24
    • 4344642807 scopus 로고    scopus 로고
    • Learning hybrid Neuro-Fuzzy classifier models from data: To combine or not to combine?
    • B. Gabrys Learning hybrid Neuro-Fuzzy classifier models from data: To combine or not to combine? Fuzzy Sets and Systems 147 2004 39 56
    • (2004) Fuzzy Sets and Systems , vol.147 , pp. 39-56
    • Gabrys, B.1
  • 25
    • 33746672590 scopus 로고    scopus 로고
    • Genetic algorithms in classifier fusion
    • DOI 10.1016/j.asoc.2005.11.001, PII S1568494605000840
    • B. Gabrys, and D. Ruta Genetic algorithms in classifier fusion Applied Soft Computing 6 4 2006 337 347 (Pubitemid 44160505)
    • (2006) Applied Soft Computing Journal , vol.6 , Issue.4 , pp. 337-347
    • Gabrys, B.1    Ruta, D.2
  • 26
  • 28
    • 30344458742 scopus 로고    scopus 로고
    • The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming
    • DOI 10.1016/j.artmed.2005.06.002, PII S0933365705000795
    • J.-H. Hong, and S.-B. Cho The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming Artificial Intelligence in Medicine 36 1 2006 43 58 (Pubitemid 43068582)
    • (2006) Artificial Intelligence in Medicine , vol.36 , Issue.1 , pp. 43-58
    • Hong, J.-H.1    Cho, S.-B.2
  • 29
  • 31
    • 70949095442 scopus 로고    scopus 로고
    • Architecture for development of adaptive on-line prediction models
    • P. Kadlec, and B. Gabrys Architecture for development of adaptive on-line prediction models Memetic Computing 1 4 2009 241 269
    • (2009) Memetic Computing , vol.1 , Issue.4 , pp. 241-269
    • Kadlec, P.1    Gabrys, B.2
  • 34
    • 77952545391 scopus 로고    scopus 로고
    • Meta-learning for time series forecasting and forecast combination
    • C. Lemke, and B. Gabrys Meta-learning for time series forecasting and forecast combination Neurocomputing 73 10-12 2010 2006 2016
    • (2010) Neurocomputing , vol.73 , Issue.1012 , pp. 2006-2016
    • Lemke, C.1    Gabrys, B.2
  • 36
    • 0345040873 scopus 로고    scopus 로고
    • Classification and regression by random forest
    • A. Liaw, and M. Wiener Classification and regression by random forest R News 2 3 2002 18 22
    • (2002) R News , vol.2 , Issue.3 , pp. 18-22
    • Liaw, A.1    Wiener, M.2
  • 38
  • 39
    • 84865244968 scopus 로고    scopus 로고
    • Backpropagation in decision trees for regression
    • Machine Learning: ECML 2001
    • V. Medina-Chico, A. Suarez, and J.F. Lutsko Backpropagation in decision trees for regression L. De Raedt, P. Flach, ECML 2001 LNAI Vol. 2167 2001 Springer Verlag 348 359 (Pubitemid 33331082)
    • (2001) Lecture Notes in Computer Science , Issue.2167 , pp. 348-359
    • Medina-Chico, V.1    Suarez, A.2    Lutsko, J.F.3
  • 40
    • 0001495905 scopus 로고
    • Learning with continuous classes
    • World Scientific Singapore
    • J.R. Quinlan Learning with continuous classes Proceedings of the AI'92 1992 World Scientific Singapore (pp. 343-348)
    • (1992) Proceedings of the AI'92
    • Quinlan, J.R.1
  • 41
    • 84865206107 scopus 로고    scopus 로고
    • Crosstrained ensemble of neural networks for robust time series prediction
    • Ruta, D. (2006). Crosstrained ensemble of neural networks for robust time series prediction. Technical Report, NiSIS 2006.
    • (2006) Technical Report, NiSIS 2006
    • Ruta, D.1
  • 43
    • 0036026248 scopus 로고    scopus 로고
    • A theoretical analysis of the limits of majority voting errors for multiple classifier systems
    • DOI 10.1007/s100440200030
    • D. Ruta, and B. Gabrys A theoretical analysis of the limits of majority voting errors for multiple classifier systems Pattern Analysis and Applications 5 4 2002 333 350 (Pubitemid 36177590)
    • (2002) Pattern Analysis and Applications , vol.5 , Issue.4 , pp. 333-350
    • Ruta, D.1    Gabrys, B.2
  • 44
    • 33746723714 scopus 로고    scopus 로고
    • Set analysis of coincident errors and its applications for combining classifiers
    • D. Ruta, and B. Gabrys Set analysis of coincident errors and its applications for combining classifiers D. Chen, X. Cheng, Pattern recognition and string matching 2002 Kluwer Academic Publishers
    • (2002) Pattern Recognition and String Matching
    • Ruta, D.1    Gabrys, B.2
  • 46
    • 10444224737 scopus 로고    scopus 로고
    • Classifier selection for majority voting
    • D. Ruta, and B. Gabrys Classifier selection for majority voting Information Fusion 6 1 2005 63 81
    • (2005) Information Fusion , vol.6 , Issue.1 , pp. 63-81
    • Ruta, D.1    Gabrys, B.2
  • 48
    • 35148881801 scopus 로고    scopus 로고
    • Combination of multi level forecasts
    • DOI 10.1007/s11265-007-0076-3, Special Issue: Data Fusion for Medical, Industrial, and Environmental Applications. Guest Editors: Danilo Mandic and Dragan Obradovic
    • S. Riedel, and B. Gabrys Combination of multi level forecasts International Journal of VLSI Signal Processing Systems 49 2 2007 265 280 (Pubitemid 47536852)
    • (2007) Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology , vol.49 , Issue.2 , pp. 265-280
    • Riedel, S.1    Gabrys, B.2
  • 53
    • 43949130125 scopus 로고    scopus 로고
    • Polynomial regression with automated degree: A function approximator for autonomous agents
    • D. Stronger, and P. Stone Polynomial regression with automated degree: A function approximator for autonomous agents Artificial Intelligence Tools 17 1 2008
    • (2008) Artificial Intelligence Tools , vol.17 , Issue.1
    • Stronger, D.1    Stone, P.2
  • 56
    • 26944501740 scopus 로고    scopus 로고
    • Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods
    • G. Valentini, and T.G. Dietterich Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods Journal of Machine Learning Research 5 2004 725 775
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 725-775
    • Valentini, G.1    Dietterich, T.G.2
  • 58
    • 0026928374 scopus 로고
    • Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
    • L.-X. Wang, and J.M. Mendel Fuzzy basis functions, universal approximation, and orthogonal least-squares learning IEEE Transactions on Neural Networks 3 5 1992 807 814
    • (1992) IEEE Transactions on Neural Networks , vol.3 , Issue.5 , pp. 807-814
    • Wang, L.-X.1    Mendel, J.M.2
  • 59
    • 59049094484 scopus 로고    scopus 로고
    • Modeling slump of concrete with fly ash and superplasticizer
    • I-C. Yeh Modeling slump of concrete with fly ash and superplasticizer Computers and Concrete 5 6 2008 559 572
    • (2008) Computers and Concrete , vol.5 , Issue.6 , pp. 559-572
    • Yeh, I.-C.1
  • 60
    • 2442688288 scopus 로고    scopus 로고
    • Genetic programming in classifying large-scale data: An ensemble method
    • Y. Zhang, and S. Bhattacharyya Genetic programming in classifying large-scale data: An ensemble method Information Sciences 163 1-3 2004 85 101
    • (2004) Information Sciences , vol.163 , Issue.13 , pp. 85-101
    • Zhang, Y.1    Bhattacharyya, S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.