This site has been permanently archived. This is a static copy provided by the University of Southampton.

TY - GEN ID - cogprints7180 UR - http://cogprints.org/7180/ A1 - Iqbal, Ridwan Al TI - A Generalized Method for Integrating Rule-based Knowledge into Inductive Methods Through Virtual Sample Creation Y1 - 2011/01/25/ N2 - Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for classification. Methods that use domain knowledge have been shown to perform better than inductive learners. However, there is no general method to include domain knowledge into all inductive learning algorithms as all hybrid methods are highly specialized for a particular algorithm. We present an algorithm that will take domain knowledge in the form of propositional rules, generate artificial examples from the rules and also remove instances likely to be flawed. This enriched dataset then can be used by any learning algorithm. Experimental results of different scenarios are shown that demonstrate this method to be more effective than simple inductive learning. AV - public KW - Rule based learning KW - hybrid learning KW - virtual sample KW - virtual example KW - artificial sample KW - artificial example KW - pruning dataset ER -