Ioannis Hatzilygeroudis and Jim Prentzas, University of Patras and Computer Technology Institute, Greece
Neurules are a kind of hybrid rules integrating neurocomputing and production rules. Each neurule is represented as an adaline unit. Thus, the corresponding rule base consists of a number of autonomous adaline units (neurules). Due to this fact, a modular and natural rule base is constructed, in contrast to existing connectionist rule bases. In this paper, we present a method for generating neurules from empirical data. We overcome the difficulty of the adaline unit to classify non-separable training examples by introducing the notion of closeness between training examples and splitting each training set into subsets of close examples.