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

By Ridwan Al Iqbal


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

Topics: Artificial Intelligence, Machine Learning
Year: 2011
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    1. (1991). A knowledge-intensive approach to learning relational concepts.
    2. (1994). An Introduction to Computational Learning Theory.
    3. (1997). Artificial neural networks.
    4. (1993). C4.5: Programs for Machine Learning.
    5. (1992). Combining Symbolic and Neural Learning to Revise Probabilistic Theories.
    6. (1997). Comprehensible knowledge discovery in databases. In
    7. (1997). Concept learning and general to specific ordering.
    8. (1998). Incorporating Prior Information in Machine Learning by Creating Virtual Examples.
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