Automatic Generation of Symbolic Multiattribute Ordinal Knowledge‐Based DSSs: Methodology and Applications*

A learning-by-example algorithm, the ordinal learning model (OLM), that automatically generates symbolic rule-bases from examples was applied to four real-world multiattribute ordinal problem domains. The model automatically generates consistent and irredundant symbolic classification rules that mimic, in many aspects, the behavior of human subjects who solved similar problems during empirical studies. The OLM's performance is compared with those of regression analysis and with C4, a well-known symbolic learning-by-example decision tree building algorithm. The OLM uses mainly comparison operations and does not attempt to optimize the rule-bases it generates. Yet, the results show that the OLM's predictions are very accurate and the resulting rule-bases are relatively compact. The time required for constructing the rule-bases via the OLM was very competitive as well.

[1]  Leon Sterling,et al.  Learning and classification of monotonic ordinal concepts , 1989, Comput. Intell..

[2]  R. Hogarth,et al.  Unit weighting schemes for decision making , 1975 .

[3]  David W. Aha,et al.  Learning Representative Exemplars of Concepts: An Initial Case Study , 1987 .

[4]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[5]  Giulia Pagallo,et al.  Learning DNF by Decision Trees , 1989, IJCAI.

[6]  Oleg I. Larichev,et al.  Systematic research into human behavior in multiattribute object classification problems , 1988 .

[7]  Oleg I. Larichev,et al.  Limits to decision-making ability in direct multiattribute alternative evaluation , 1988 .

[8]  Derek L. Nazareth,et al.  Issues in the Verification of Knowledge in Rule-Based Systems , 1989, Int. J. Man Mach. Stud..

[9]  J. Jacoby,et al.  Brand Choice Behavior as a Function of Information Load: Replication and Extension , 1974 .

[10]  R. Dawes,et al.  Linear models in decision making. , 1974 .

[11]  James N. MacGregor.  The Effects of Order on Learning Classifications by Example: Heuristics for Finding the Optimal Order , 1988, Artif. Intell..

[12]  Yoav Ganzach.  Goals as Determinants of Nonlinear Noncompensatory Judgment Strategies: Leniency vs Strictness , 1993 .

[13]  Howard Wainer,et al.  Estimating Coefficients in Linear Models: It Don't Make No Nevermind , 1976 .

[14]  H. M. Moshkovich,et al.  Modeling multiattribute information processing strategies in a binary decision task , 1980 .