메뉴 건너뛰기




Volumn 35, Issue 4, 2008, Pages 1513-1532

Evolving model trees for mining data sets with continuous-valued classes

Author keywords

Continuous valued classes; Data mining; Genetic programming; Model trees

Indexed keywords

ALGORITHMS; BIOELECTRIC PHENOMENA; CHLORINE COMPOUNDS; COMPUTER PROGRAMMING; CONTROL SYSTEM ANALYSIS; CONTROL THEORY; DIESEL ENGINES; GENETIC ALGORITHMS; MATHEMATICAL MODELS; MATHEMATICAL OPERATORS; PARAMETER ESTIMATION; PERSONNEL TRAINING; SET THEORY; STRUCTURAL DESIGN; STRUCTURAL OPTIMIZATION;

EID: 48749127627     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.08.060     Document Type: Article
Times cited : (16)

References (36)
  • 1
    • 33645964460 scopus 로고    scopus 로고
    • Abass H., Saker R., and Newton C. (Eds), Idea Publishing Group
    • In: Abass H., Saker R., and Newton C. (Eds). Data mining: A heuristic approach (2002), Idea Publishing Group
    • (2002) Data mining: A heuristic approach
  • 5
    • 48749090620 scopus 로고    scopus 로고
    • Blake, C., Keogh, E., & Merz, C. (1998). UCI repository of machine learning databases. Department of Information and Computer Science, University of California Irvine. http://www.ics.uci.edu/~mlearnMLRepository.
    • Blake, C., Keogh, E., & Merz, C. (1998). UCI repository of machine learning databases. Department of Information and Computer Science, University of California Irvine. http://www.ics.uci.edu/~mlearnMLRepository.
  • 8
    • 34249966007 scopus 로고
    • The CN2 induction algorithm
    • Clark P., and Niblitt T. The CN2 induction algorithm. Machine Learning 3 (1989) 261-283
    • (1989) Machine Learning , vol.3 , pp. 261-283
    • Clark, P.1    Niblitt, T.2
  • 10
    • 84956852101 scopus 로고    scopus 로고
    • Adapting the fitness function in GP for data mining
    • Genetic programming. Poli R., Nordin P., Langdon W., and Fogarty T. (Eds), Springer-Verlag, Goteborg, Sweden
    • Eggermont J., Eiben A., and van Hemert J. Adapting the fitness function in GP for data mining. In: Poli R., Nordin P., Langdon W., and Fogarty T. (Eds). Genetic programming. Proceedings of EuroGP'99, 26-27 1999 Vol. 1598 (1999), Springer-Verlag, Goteborg, Sweden 193-202. [Online]. Available from citeseer.nj.nec.com/eggermont99adapting.html
    • (1999) Proceedings of EuroGP'99, 26-27 1999 , vol.1598 , pp. 193-202
    • Eggermont, J.1    Eiben, A.2    van Hemert, J.3
  • 11
    • 0036608586 scopus 로고    scopus 로고
    • Supervised training using an unsupervised approach to active learning
    • Kluwer Academic Publishers, Netherlands
    • Engelbrecht A., and Brits R. Supervised training using an unsupervised approach to active learning. Neural processing letters (2002), Kluwer Academic Publishers, Netherlands 247-269
    • (2002) Neural processing letters , pp. 247-269
    • Engelbrecht, A.1    Brits, R.2
  • 12
    • 2942586010 scopus 로고    scopus 로고
    • A building-block approach to genetic programming for rule discovery
    • Abbass C.N.H.A., and Sarker R.A. (Eds), Idea Group Publishing
    • Engelbrecht A., Schoeman L., and Rouwhorst S. A building-block approach to genetic programming for rule discovery. In: Abbass C.N.H.A., and Sarker R.A. (Eds). Data mining: A heuristic approach (2001), Idea Group Publishing 174-190
    • (2001) Data mining: A heuristic approach , pp. 174-190
    • Engelbrecht, A.1    Schoeman, L.2    Rouwhorst, S.3
  • 13
    • 0000014486 scopus 로고
    • Cluster analysis of multivariate data: Efficiency versus interpretability of classification
    • Forgy E. Cluster analysis of multivariate data: Efficiency versus interpretability of classification. Biometrics 21 (1965) 768-769
    • (1965) Biometrics , vol.21 , pp. 768-769
    • Forgy, E.1
  • 14
    • 0002344899 scopus 로고    scopus 로고
    • A genetic programming framework for two data mining tasks: Classification and generalized rule induction
    • Koza J., Deb K., Dorigo M., Fogel D., Garzon M., Iba H., and Riolo R. (Eds), Morgan Kaufmann
    • Freitas A. A genetic programming framework for two data mining tasks: Classification and generalized rule induction. In: Koza J., Deb K., Dorigo M., Fogel D., Garzon M., Iba H., and Riolo R. (Eds). Genetic programming 1997: Proceedings of the second annual conference, Stanford University, 13-16 1997 (1997), Morgan Kaufmann 96-101. [Online]. Available from citeseer.nj.nec.com/43454.html
    • (1997) Genetic programming 1997: Proceedings of the second annual conference, Stanford University, 13-16 1997 , pp. 96-101
    • Freitas, A.1
  • 17
    • 0001533001 scopus 로고    scopus 로고
    • Genetic recursive regression for modelling and forecasting real-world chaotic time series
    • Geom Y. Genetic recursive regression for modelling and forecasting real-world chaotic time series. Advances in Genetic Programming 3 (1999) 401-423
    • (1999) Advances in Genetic Programming , vol.3 , pp. 401-423
    • Geom, Y.1
  • 18
    • 0002819121 scopus 로고
    • A comparitive analysis of selection schemes used in genetic algorithms
    • Rawlins G. (Ed), Morgan-Kaufman
    • Goldberg G., and Deb K. A comparitive analysis of selection schemes used in genetic algorithms. In: Rawlins G. (Ed). Foundations of genetic algorithms (1991), Morgan-Kaufman 69-93
    • (1991) Foundations of genetic algorithms , pp. 69-93
    • Goldberg, G.1    Deb, K.2
  • 19
    • 84901402398 scopus 로고    scopus 로고
    • Genetic evolution of regression models for business and economic forecasting
    • IEEE Press
    • Kaboudan M. Genetic evolution of regression models for business and economic forecasting. Proceedings of the congress on evolutionary computation vol. 2 (1999), IEEE Press 1260-1268
    • (1999) Proceedings of the congress on evolutionary computation , vol.2 , pp. 1260-1268
    • Kaboudan, M.1
  • 23
    • 48749090868 scopus 로고    scopus 로고
    • Potgieter, G. (2003). Mining continuous classes using evolutionary computing. Master's thesis. Department of Computer Science, University of Pretoria.
    • Potgieter, G. (2003). Mining continuous classes using evolutionary computing. Master's thesis. Department of Computer Science, University of Pretoria.
  • 24
    • 48749084763 scopus 로고    scopus 로고
    • Potgieter, G., & Engelbrecht, A. (2002). Structural optimization of learned polynomial expressions using genetic algorithms. In Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning.
    • Potgieter, G., & Engelbrecht, A. (2002). Structural optimization of learned polynomial expressions using genetic algorithms. In Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning.
  • 25
    • 33947707329 scopus 로고    scopus 로고
    • Genetic algorithms for the structural optimisation of learned polynomial expressions
    • Potgieter G., and Engelbrecht A. Genetic algorithms for the structural optimisation of learned polynomial expressions. Applied Mathematics and Computation 186 (2007) 1441-1466
    • (2007) Applied Mathematics and Computation , vol.186 , pp. 1441-1466
    • Potgieter, G.1    Engelbrecht, A.2
  • 26
    • 0001857179 scopus 로고
    • Learning efficient classification procedures and their application to chess endgames
    • Michalski R., Carbonell J., and Mitchell T. (Eds), Tioga Press, Palo Alto
    • Quinlan J. Learning efficient classification procedures and their application to chess endgames. In: Michalski R., Carbonell J., and Mitchell T. (Eds). Machine learning: An artificial intelligence approach vol. 1 (1983), Tioga Press, Palo Alto 463-482
    • (1983) Machine learning: An artificial intelligence approach , vol.1 , pp. 463-482
    • Quinlan, J.1
  • 27
    • 0001495905 scopus 로고
    • Learning with continuous classes
    • Adams, and Sterling (Eds), World Scientific, Singapore
    • Quinlan J. Learning with continuous classes. In: Adams, and Sterling (Eds). Proceedings of artificial intelligence'92 (1992), World Scientific, Singapore 343-348
    • (1992) Proceedings of artificial intelligence'92 , pp. 343-348
    • Quinlan, J.1
  • 31
    • 0029185114 scopus 로고
    • Use of quasi-Newton method in a feedforward neural network construction algorithm
    • Setiono R., and Hui L. Use of quasi-Newton method in a feedforward neural network construction algorithm. IEEE Transactions on Neural Networks 6 1 (1995) 273-277
    • (1995) IEEE Transactions on Neural Networks , vol.6 , Issue.1 , pp. 273-277
    • Setiono, R.1    Hui, L.2
  • 32
    • 84867826448 scopus 로고    scopus 로고
    • Pruned neural networks for regression
    • Proceedings of the 6th Pacific Rim conference on artificial intelligence, PRICAI 2000. Mizoguchi R., and Staney J. (Eds), Springer, Melbourne, Australia
    • Setiono R., and Leow W. Pruned neural networks for regression. In: Mizoguchi R., and Staney J. (Eds). Proceedings of the 6th Pacific Rim conference on artificial intelligence, PRICAI 2000. Lecture Notes in AI Vol. 1886 (2000), Springer, Melbourne, Australia 500-509
    • (2000) Lecture Notes in AI , vol.1886 , pp. 500-509
    • Setiono, R.1    Leow, W.2
  • 33
    • 0036565303 scopus 로고    scopus 로고
    • Extraction of rules from artificial neural networks for nonlinear regression
    • Setiono R., Leow W., and Zurada J. Extraction of rules from artificial neural networks for nonlinear regression. IEEE Transactions on Neural Networks 13 3 (2002) 564-577
    • (2002) IEEE Transactions on Neural Networks , vol.13 , Issue.3 , pp. 564-577
    • Setiono, R.1    Leow, W.2    Zurada, J.3
  • 34
    • 0042655434 scopus 로고
    • Refining symbolic knowledge using neural networks
    • Towell C., and Shavlik J. Refining symbolic knowledge using neural networks. Machine Learning 12 (1994) 321-331
    • (1994) Machine Learning , vol.12 , pp. 321-331
    • Towell, C.1    Shavlik, J.2
  • 35
    • 48749092497 scopus 로고    scopus 로고
    • Viktor, H., Engelbrecht, A., & Cloete, I. (1995). Reduction of symbolic rules from neural networks using sensitivity analysis. In Proceedings of the IEEE international joint conference on neural networks, Perth, Australia (pp. 1022-1026).
    • Viktor, H., Engelbrecht, A., & Cloete, I. (1995). Reduction of symbolic rules from neural networks using sensitivity analysis. In Proceedings of the IEEE international joint conference on neural networks, Perth, Australia (pp. 1022-1026).


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.