Specifying and Learning Inductive Learning Systems Using Ontologies

A. Suyama and T. Yamaguchi

Here is presented a platform for automatic composition of inductive learning systems using ontologies called CAMLET, based on knowledge modeling and ontologies engineering technique. CAMLET constructs an inductive learning system with better competence to a given data set, using process and object ontologies. Afterwards, CAMLET instantiates and refines a constructed system based on the following refinement strategies: greedy alteration, random generation and heuristic alteration. Using the UCI repository of ML databases and domain theories, experimental results have shown us that CAMLET supports a user in constructing a inductive learning system with best competence.


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