SIA: A supervised inductive algorithm with genetic search for learning attributes based concepts

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 667 LNAI, Issue , 1993, Pages 280-296

  Venturini, Gilles ^a  

^a UFR 919 Laboratoire d'Informatique Pour la Mécanique et les Sciences de l'Ingénieur   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; GENETIC ALGORITHMS; MACHINE LEARNING; SUPERVISED LEARNING;

GENETIC LEARNING; GENETIC PROCESS; INDUCTIVE ALGORITHMS; LEARNING TASKS; NOISE-TOLERANT; RULE EVALUATION; TREES ALGORITHM; VARIABLE NUMBER;

LEARNING ALGORITHMS;

EID: 84971641220     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-56602-3_142     Document Type: Conference Paper

References (23)

1. Aubusson de Cavarlay B. (1987a), La diversité du traitement pénal, Données sociales 19, 589-593.
2. Aubusson de Cavarlay B. (1987b), Les filières pénales, CESDIP, Déviance et Contrôle Social 43.
3. Bala J., De Jong K.A. and Pachowicz P. (1991), Learning noise tolerant classification procedures by integrating inductive learning and genetic algorithms, Proceedings of the First International Workshop on Multistrategy Learning 1991, R.S. Michalski and G. Tecuci (Eds), 316-323.
4. Bonelli P. and Parodi A. (1991), An efficient classifier system and its experimental comparison with two representative learning methods on three medical domains, Proceedings of the Fourth International Conference on Genetic Algorithms, R.K. Belew and L.B. Booker (Eds), 288-295, Morgan Kaufmann.
5. De Jong K. (1988). Learning with Genetic Algorithms: An overview. Machine Learning 3, 121-138: Kluwer Academic.
6. th International Joint Conference on Artificial Intelligence 1991, J. Mylopoulos and R. Reiter (Eds), 651-656, Morgan Kaufmann.
7. Gams M. and Lavrac N. (1987), Review of five empirical learning systems within a proposed schemata, Progress in Machine Learning, I. Bratko and N. Lavrac (Eds), 46-66, Sigma Press.
8. Goldberg D.E. (1989). Genetic Algorithms in Search, Optimisation and Machine Learning: Addison Wesley.
9. Grefenstette J.J. (1989), A system for learning control strategies with genetic algorithms. In Proceedings of the third International Conference on Genetic Algorithms, J.D. Schaffer (Ed), 183-190, Morgan Kaufmann.
10. Holland J.H. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.
11. Janikow C.Z. (1992), Combining competition and cooperation in supervised inductive learning, Proceedings of the Ninth International Workshop on Machine Learning 1992, D. Sleeman and P. Edwards (Eds), 241-248, Morgan Kaufmann.
12. Kononenko I. and Kovacic M. (1992), Learning as optimization: stochastic generation of multiple knowledge, Proceedings of the Ninth International Workshop on Machine Learning 1992, D. Sleeman and P. Edwards (Eds), 257-262, Morgan Kaufmann.
13. McCallum J.H. and Spackman K.A. (1990), Using genetic algorithms to learn disjunctive rules from examples, Proceedings of the Seventh International Conference on Machine Learning 1990, B.W. Porter and R.J. Mooney (Eds.), 153-159, Morgan Kaufmann.
14. Michalski R.S., Mozetic I., Hong J. and Lavrac N. (1986), The multi-purpose incremental learning system AQ15 and its testing application to three medical domains, Proceedings of AAAI-86 Fifth National Conference on Artificial Intelligence, 1041-1045, Morgan Kaufmann.
15. Quinlan J.R. (1986), Induction of decision trees, Machine Learning 1, 1.
16. Quinlan J.R. (1987), Generating production rules from decision trees, Proceedings of the Tenth International Joint Conference on Artificial Intelligence 1987, J. McDermott (Ed), 304-307, Morgan Kaufmann.
17. Quinlan J.R. (1988), An empirical comparison of genetic and decision trees classifiers, Proceedings of the Fifth International Conference on Machine Learning 1988, J. Laird (Eds), 135-141, Morgan Kaufmann.
18. Salzberg S. (1991), A nearest hyperrectangle learning method, Machine Learning 6, 251-276.
19. Vafaie H. and De Jong K. (1991), Improving the performance of a rule induction system using genetic algorithms, Proceedings of the First International Workshop on Multistrategy Learning 1991, R.S. Michalski and G. Tecuci (Eds), 305-315.
20. Venturini G. (1992), AGIL: solving the exploration versus exploitation dilemma in a simple classifier system applied to simulated robotics, Proceedings of the Ninth International Workshop on Machine Learning 1992, D. Sleeman and P. Edwards (Eds), 458-463, Morgan Kaufmann.
21. Weinberg J.B., Biswas G. and Koller G.R. (1992), Conceptual clustering with systematic missing values, Proceedings of the Ninth International Workshop on Machine Learning 1992, D. Sleeman and P. Edwards (Eds), 464-469, Morgan Kaufmann.
22. Wilson S.W. (1987), Quasi-Darwinian Learning in a Classifier System, Proceeding of the Fourth International Workshop on Machine Learning 1987, P. Langley (Ed), 59-65, Morgan Kaufmann.
23. Wnek J. and Michalski R.S. (1991), An experimental comparison of symbolic and subsymbolic learning paradigms: phase I - learning logic-style concepts, Proceedings of the First International Workshop on Multistrategy Learning 1991, R.S. Michalski and G. Tecuci (Eds), 324-339.