Mapping Learning Algorithms on Data, a Useful Step for Optimizing Performances and Their Comparison

Abstract

In this paper, we propose a novel methodology to map learning algorithms on data (performance map) in order to gain more insights into the distribution of their performances across their parameter space. This methodology provides useful information when selecting a learner's best configuration for the data at hand and it also enhances the comparison of learners across learning contexts. In order to explain the proposed methodology, the study introduces the notions of learning context, performance map, and high-performance function. It then applies these concepts to a variety of learning contexts to show how their use can provide more insights into a learner's behavior and can enhance the comparison of learners across learning contexts. The study is completed by an extensive experimental study describing how the proposed methodology can be applied.