Here is presented CAMLET that is a platform for automatic composition of inductive applications using ontologies that specify inductive learning methods. CAMLET constructs inductive applications using process and object ontologies. After instantiating, compiling and executing the basic design specification, CAMLET refines the specification based on the following refinement strategies: crossover of control structures, random generation and process replacement by heuristic. Using fourteen different data sets form the UCI repository of ML databases and and the database on meningoencephalitis with human expert’s evaluation, experimental results have shown us that CAMLET supports a user in constructing inductive applications with better competence.