Parallel model refinement of inductive applications using method repository

Here is presented parallel CAMLET that is a platform for automatic composition of inductive applications with method repositories that organize many inductive learning methods. After having implemented CAMLET on a UNIX parallel machine with Perl and C languages, we have done the case studies of constructing inductive applications for eight different data sets from the StatLog repository. To find out an efficient search method, we have done the following experiments: a random search, a GA based search, and two hybrid searches with unifying each global search and the local search which uses meta-rules for refining a specification. That have shown us that the hybrid search works better than the other search methods. Furthermore, comparing the accuracy of inductive applications composed by parallel CMALET with that of popular twenty four inductive algorithms, we have shown that parallel CAMLET support a user in doing model selection in KDD engineering processes.