INFER: an adaptative decision support system based on the probabilistic approximate classification
6th Internation Workshop Vol. 1 on Expert Systems & Their Applications
A new version of the rule induction system LERS
Fundamenta Informaticae
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
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This work introduces a generalization of the algorithm LEM3, an incremental learning system of production rules from examples, based on the Boolean Approximation Space introduced by Pawlak. The generalization is supported in the Stochastic Approximation Space introduced by Wong and Ziarko. In this paper, stochastic limits in the precision of the upper and lower approximations of a class are addressed. These allow the generation of certain rules with a certainty level β (0.5≤β≤1). Also the modifications in LEM3 necessary in order to handle examples with missing attribute values are introduced.