Introducing multi-dimensional hierarchical classification: Characterization, solving strategies and performance measures

Abstract

Classification problems where there exist multiple class variables that need to be jointly predicted are known as Multi-dimensional classification problems. If the labels of these class variables are organized as hierarchies, we can take advantage of specific strategies designed for the Hierarchical classification paradigm. In this paper we present the Multi-dimensional hierarchical classification (MDHC) paradigm, a result of the combination of Multi-dimensional and Hierarchical classification paradigms. We propose four MDHC learning strategies which are designed to exploit the particularities of this new paradigm, combining characteristics of Multi-dimensional and Hierarchical classification strategies. Along with these strategies, we present a framework for classifier comparison in which we use a set of performance measures specifically designed for MDHC, and a procedure to create MDHC synthetic scenarios. Using this framework and the performance measures presented, we study how characteristics of the MDHC problems influence the performance of the different MDHC strategies proposed, and compare them to other non-MDHC strategies.