Preliminary approach on synthetic data sets generation based on class separability measure

Usually, performance of classifiers is evaluated on real-world problems that mainly belong to public repositories. However, we ignore the inherent properties of these data and how they affect classifier behavior. Also, the high cost or the difficulty of experiments hinder the data collection, leading to complex data sets characterized by few instances, missing values, and imprecise data. The generation of synthetic data sets solves both issues and allows us to build problems with a minor cost and whose characteristics are predefined. This is useful to test system limitations in a controlled framework. This paper proposes to generate synthetic data sets based on data complexity. We rely on the length of the class boundary to build the data sets, obtaining a preliminary set of benchmarks to assess classifier accuracy. The study can be further matured to identify regions of competence for classifiers.