Learning Heterogeneous Similarity Measures for Hybrid-Recommendations in Meta-Mining

The notion of meta-mining has appeared recently and extends traditional meta-learning in two ways. First it provides support for the whole data-mining process. Second it pries open the so called algorithm black-box approach where algorithms and workflows also have descriptors. With the availability of descriptors both for datasets and data-mining workflows we are faced with a problem the nature of which is much more similar to those appearing in recommendation systems. In order to account for the meta-mining specificities we derive a novel metric-based-learning recommender approach. Our method learns two homogeneous metrics, one in the dataset and one in the workflow space, and a heterogeneous one in the dataset-workflow space. All learned metrics reflect similarities established from the dataset-workflow preference matrix. The latter is constructed from the performance results obtained by the application of workflows to datasets. We demonstrate our method on meta-mining over biological (microarray datasets) problems. The application of our method is not limited to the meta-mining problem, its formulation is general enough so that it can be applied on problems with similar requirements.