Knowledge acquisition from questionnaire data using simulated breeding and inductive learning methods

Expert Systems with Applications

Volumn 11, Issue 4 SPEC. ISS., 1996, Pages 507-518

  Terano, Takao ^a   Ishino, Yoko ^b  

^a UNIVERSITY OF TSUKUBA   (Japan)

^b UNIVERSITY OF TOKYO   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION MAKING; GENETIC ALGORITHMS; LEARNING SYSTEMS;

INDUCTIVE LEARNING; QUESTIONNAIRE DATA; SIMULATED BREEDING;

KNOWLEDGE ACQUISITION;

EID: 0030401208     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0957-4174(96)00066-8     Document Type: Article

References (39)

1. Aaker, D. S. & Day, G. S. (1980). Marketing research: private and public sector decisons. New York: Wiley.
2. Aiba, H. & Terano, T. (1996). A computational model for distributed knowledge systems with learning mechanisms. Expert Systems With Applications, 10(3/4), 417-427.
3. Bala, J. W., De Jong, K. & Pachowicz, P. W. (1994). Multistrategy learning from engineering data by integrating inductive generalization and genetic algorithms. In R. Michalski & G. Tecuci (Eds), Machine learning IV: a multistrategy approach, pp. 471-488. CA: Morgan/Kaufmann.
4. Bala, J. W., Huang, J., Vafaie, H., De Jong, K. & Wechsler, H. (1995). Hybrid learning using genetic algorithms and decision trees for pattern recognition. In Proceedings of 14th International Joint Conference on Artificial Intelligence, pp. 719-724.
5. Bond, A. & Gasser, L. (Eds) (1988). Readings in distributed artificial intelligence. CA: Morgan/Kaufmann.
6. Buntine, W. & Niblett, T. (1992). A further comparison of splitting rules for decision-tree induction. Machine Learning, 8, 75-86.
7. Clark, L. A. & Pregibon, D. (1992). Tree-based model. In J. M. Chambers & T. J. Hastie (Eds), Statistical models in S. AT&T Bell Laboratories.
8. Davis, L. (Ed.) (1991). Handbook of genetic algorithms. Berlin: Van Nostrand Reinhold.
9. Dawkins, R. (1986). The blind watchmaker. W. W. Norton.
10. De Jong, K. A. & Sears, W. M. (1991). Learning concept classification rules using genetic algorithms. In Proceedings 12th International Joint Conference on Artificial Intelligence, pp. 651-656.
11. Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. MA: Addison-Wesley.
12. Hirsh, H. & Noordewier, M. (1994) Using background knowledge to improve inductive learning: a case study in molecular biology. IEEE Expert, 9(5), 3-6.
13. Holland, J. H. (1992). Adaptation in natural and artificial systems (2nd edition). MA: MIT Press.
14. Janikow, C. Z. (1993). A knowledge-intensive generic algorithm for supervised learning. Machine Learning, 13, 189-228.
15. John, G. H., Kohavi, R. & Pfleger, K. (1994). Irrelevant feature and the subset selection problem. In Proceedings 11th International Workshop on Machne Learning, pp. 121-129.
16. Kendall, M. (1980). Multivariate analysis (2nd edition). London: Charles Griffin.
17. Kira, K. & Rendell, L. A. (1992). The feature selection problem: traditional methods and a new algorithm. In Proceedings 10th National Conference on Artificial Intelligence, pp. 129-134.
18. Lesser, V. R. (1995). Multiagent systems: an emerging subdiscipline of AI. ACM Computing Surveys, 27(3), 340-342.
19. Liu, W. Z. & White, A. P. (1994). The importance of attribute selection measures in decision tree induction. Machine Learning, 15, 25-41.
20. Minger, J. (1989a). An empirical comparison of selection measures for decision-tree induction. Machine Learning, 3, 319-342.
21. Minger, J. (1989b). An empirical comparison of pruning methods for decision-tree induction. Machine Learning, 4, 227-243.
22. Quinlan, J. R. (1986). Induction for decision trees. Machine Learning, 1(1), 81-106.
23. Quinlan, J. R. (1993). C4.5: Programs for machine learning. CA: Morgan/Kaufmann.
24. Saaty, T. L. (1980). The analytic hierarchy process - planning, priority selling, resource allocation. New York, McGraw-Hill.
25. SAS (1985). SAS user's guide. SAS Institute Inc.
26. Schaffer, C. (1993). Overfitting avoidance as bias. Machine Learning, 10, 153-178.
27. Shapiro, A. D. (1987). Structured induction in expert systems. MA: Addison-Wesley.
28. Sims, K. (1992). Interactive evolution of dynamical systems. In F. J. Varela & P. Bourgine (Eds), Toward a practice of autonomous systems - Proceeding 1st European Conference Artificial Life, pp. 171-178. MA: MIT Press.
29. Terano, T., Kusunoki, F., Ono, S. & Cho, D. (1995a). A machine learning model for analyzing performance of organizational behaviors of agents. In Proceedings 3rd Conference of the Association of Asia-Pacific Operational Research Societies (APORS), pp. 164-171.
30. Terano, T., Ishino, Y. & Yoshinaga, K. (1995b). Integrating machine learning and simulated breeding techniques to analyze the characteristics of consumer goods. In J. Biethahn & V. Nissen (Eds), Evolutionary algorithms in management applications, pp. 211-224. Berlin: Springer.
31. Terano, T. & Ishino, Y. (1995a). Data analysis using simulated breeding and inductive learning methods. In Proceedings IJCAI'95 Workshop on Data Engineering and Inductive Learning, pp. 60-69.
32. Terano, T. & Ishino, Y. (1995b). Marketing data analysis using inductive learning and genetic algorithms with interactive- and automated-phases. In Proceedings 4th IEEE International Conference on Evolutional Computing, pp. 771-776.
33. Terano, T. & Ishino, Y. (1996a). Knowledge acquisition from questionnnaire data using simulated breeding and inductive learning methods. In Proceedings 3rd World Congress on Expert Systems, pp. 869-876.
34. Terano, T. & Ishino, Y. (1996b). Interactive knowledge discovery from marketing questionnaires using simulated breeding and inductive learning methods. In Proceedings of the 2nd AAAI Conference on Knowledge Discovery from Data (KDD-96). In preparation.
35. Unemi, T. (1994). Genetic algorithms and computer graphics arts (in Japanese). Journal of Japanese Society for Artificial Intelligence, 9(4), 42-47.
36. Vafaie, H. & De Jong, K. A. (1994). Improving a rule induction system using genetic algorithms. In R. Michalski & G. Tecuci (Eds), Machine learning IV: a multistrategy approach, pp. 453-470. CA: Morgan/Kaufmann.
37. Weiss, S. M. & Kulikowski, C. A. (1991). Computer systems that learn: Classification and prediction methods from statistics, neural nets, machine learning and expert systems. CA: Morgan/Kaufmann.
38. Weiss, S. M. & Indurkhya, N. (1994). Decision tree pruning: biased or optimal? In Proceedings 12th National Conference on Artificial Intelligence, pp. 626-632.
39. Wnek, J. & Michalski, R. S. (1994). Hypothesis-driven constructive induction in AQ17-HCI: A method and experiments. Machine Learning, 14, 139-168.