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Hybrid learning methods use theoretical knowledge of a domain and a set ofclassified examples to develop a method for classification. Methods that usedomain knowledge have been shown to perform better than inductive learners.However, there is no general method to include domain knowledge into allinductive learning algorithms as all hybrid methods are highly specialized fora particular algorithm. We present an algorithm that will take domain knowledgein the form of propositional rules, generate artificial examples from the rulesand also remove instances likely to be flawed. This enriched dataset then canbe used by any learning algorithm. Experimental results of different scenariosare shown that demonstrate this method to be more effective than simpleinductive learning.

A Generalized Method for Integrating Rule-based Knowledge into Inductive Methods Through Virtual Sample Creation