Here is presented CAMLET that is a platform for automatic composition fo inductive applications using ontologies that specify inductive learning methods. CAMLET constructs a basic design specification for 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. Using fourteen different data sets from the UCI repository of ML databases and domain theories, experimental results have shown us that CAMLET supports a user in constructing inductive applications with better competence.