Automatic Generation of Symbolic Multiattribute Ordinal Knowledge-Based DSSs: Methodology and...

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. [ABSTRACT FROM AUTHOR]