A framework to decompose and develop metafeatures

Abstract

This paper proposes a framework to decompose and develop metafeatures for Metalearning (MtL) problems. Several metafeatures (also known as data characteristics) are proposed in the literature for a wide range of problems. Since MtL applicability is very general but problem dependent, researchers focus on generating specific and yet informative metafeatures for each problem. This process is carried without any sort of conceptual framework. We believe that such framework would open new horizons on the development of metafeatures and also aid the process of understanding the metafeatures already proposed in the state-of-the-art. We propose a framework with the aim of fill that gap and we show its applicability in a scenario of algorithm recommendation for regression problems.