Ontology-based meta-mining of knowledge discovery workflows

This chapter describes a principled approach to meta-learning that has three distinctive features. First, whereas most previous work on meta-learning focused exclusively on the learning task, our approach applies meta-learning to the full knowledge discovery process and is thus more aptly referred to as meta-mining. Second, traditional meta-learning regards learning algorithms as black boxes and essentially correlates properties of their input (data) with the performance of their output (learned model). We propose to tear open the black box and anal-yse algorithms in terms of their core components, their underlying assumptions, the cost functions and optimization strategies they use, and the models and de-cision boundaries they generate. Third, to ground meta-mining on a declarative representation of the data mining (dm) process and its components, we built a DM ontology and knowledge base using the Web Ontology Language (owl). The Data Mining Optimization Ontology (dmop, pronounced dee-mope)) pro-vides a unified conceptual framework for analysing dm tasks, algorithms, models