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CAMLET: A platform for automatic composition of inductive learning systems using ontologies
Description
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 applications 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 applications with best competence.
