INCREMENTAL LEARNING OF PRODUCTION RULES FROM EXAMPLES UNDER UNCERTAINTY: A ROUGH SET APPROACH

Abstract

This paper introduces an algorithm, LEM3, for incremental learning of production rules from examples. Based on the concept of rough sets introduced by Pawlak, LEM3 is capable of learning rules from consistent as well as inconsistent examples. In LEM3, rules are generated by using the rule-generating procedure implemented in a nonincremental learning program LEM2. Consequently, the rules learned by LEM3 do not use redundant attribute-value pairs. One major feature of LEM3 is the incorporation of a separate global data structure for storing information learned from new examples. The global data structure is updated on an example by example basis, and it provides all the essential information for the incremental updating of lower and upper approximations of a concept and the generating of rules. This separation of learned knowledge from rule-generating procedures provides a more modular design of learning systems.