Abstract
Knowledge acquisition by interviewing a domain expert is one of the most problematic aspects of the development of expert systems. As an alternative, methods for inducing concept descriptions from examples have proven useful in eliminating this bottleneck. In this paper, we propose a probabilistic induction method (PIM), which is an improvement of the Chan and Wong method, for detecting relevant patterns implicit in a given data set. PIM uses the technique of residual analysis and several heuristics to effectively detect complex relevant patterns and to avoid the problem of combinatorial explosion. A reasonable trade-off between the induction time and the classification ratio is achieved. Moreover, PIM quickly classifies unknown objects using classification rules converted from the positively relevant patterns detected. Three experiments are conducted to confirm the validity of PIM.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Cendrowska, J.: PRISM: An algorithm for inducing modular rules, Int. J. Man–Machine Studies 27(1987), 349–370.
Chan, K. C. C. and Wong, A. K. C.: Automatic construction of expert systems from data: A statistical approach, Proc. IJCAI’89 Workshop on Knowledge Discovery in Databases, 1989.
Chan, K. C. C. and Wong, A. K. C.: Performance analysis of a probabilistic inductive learning system, in Proc. 5th Int. Conf. Machine Learning, 1990, pp. 16–23.
Clark, P. and Niblett, T.: The CN2 induction algorithm, Machine Learning 3(1989), 261–283.
Fillmer, J. F., Mellichamp, J. M., Miller, D. M., and Narayanan, S.: An expert system for wide area network component configuration, Expert Systems9(1) (1992), 3–9.
Gisolfi, A. and Balzano, W.: Constructing and consulting the knowledge base of an expert system shell, Expert Systems 10(1) (1993), 29–34.
Grimm, F. and Bunke, H.: An expert for the selection and application of image processing subroutines, Expert Systems 10(2) (1993), 61–71.
Haberman, S. J.: The analysis of residuals in cross-classified tables, Biometrics 29(1973), 205–220.
Hong, T. P.: A Study of Parallel Processing and Noise Management on Machine Learning, Ph.D. dissertation, National Chiao-Tung University, Taiwan, R.O.C., Jan. 1992.
Hong, T. P. and Tseng, S. S.: Learning concept in parallel based upon the strategy of version space, IEEE Transactions on Knowledge and Data Engineering, 6(6) (1994), 857–867.
Hou, R. H.: A New Probabilistic Inductive Learning Method, Master’s thesis, National Chiao-Tung University, Taiwan, R.O.C., June 1992.
Jackson, A. H.: Machine learning, Expert Systems5(2) (1988), 132–149.
Kodratoff, Y. and Michalski, R. S.: Machine Learning: An Artificial Intelligence Approach, Vol. 3, Toiga, Palo Alto, CA, 1990.
Michalski, R. S., Carbonell, J. G., and Mitchell, T. M.: Machine Learning: An Artificial Intelligence Approach, Vol. 1, Toiga, Palo Alto, CA, 1983.
Michalski, R. S., Carbonell, J. G., and Mitchell, T. M.: Machine Learning: An Artificial Intelligence Approach, Vol. 2, Toiga, Palo Alto, CA, 1984.
Mingers, J.: An empirical comparison of pruning methods for decision tree induction, Machine Learning 4(1989), 319–342.
Quinlan, J. R.: Learning efficient classification procedures and their application to chess end games, in Machine Learning: An Artificial Intelligence Approach, Vol. 1, Toiga, Palo Alto, CA, 1983, pp. 463–482.
Quinlan, J. R.: The effect of noise on concept learning, in Machine Learning: An Artificial Intelligence Approach, Vol. 2, Toiga, Palo Alto, CA, 1984.
Quinlan, J. R.: Simplifying decision trees, Int. J. Man–Machine Studies 27(1987), 221–234.
Safavian, S. R. and Landgrebe, D.: A survey of decision tree classifier methodology, IEEE Transactions on Systems, Man, and Cybernetics 21(3) (1991), 660–674.
Smyth, P. and Goodman, R. M.: An information theoretic approach to rule induction from database, IEEE Transactions on Knowledge and Data Engineering4(21) (1992), 301–316.
Wang, C. H. and Tseng, S. S.: A brain tumor diagnostic system with automatic learning abilities, in 3rd IEEE Symp. Computer-Based Medical Systems Conf., 1990, pp. 313–320.
Witten, L. H. and Macdonald, B. A.: Using concept learning for knowledge acquisition, Int. J. Man–Machine Studies 29(1988), 171–196.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Hou, RH., Hong, TP., Tseng, SS. et al. A New Probabilistic Induction Method. Journal of Automated Reasoning 18, 5–24 (1997). https://doi.org/10.1023/A:1005726727996
Issue Date:
DOI: https://doi.org/10.1023/A:1005726727996