A Formalization of Multilabel Classification in Terms of Lattice Theory and Information Theory: Concerning Datasets

Abstract

Multilabel classification is a recently conceptualized task in machine learning. Contrary to most of the research that has so far focused on classification machinery, we take a data-centric approach and provide an integrative framework that blends qualitative and quantitative descriptions of multilabel data sources. By combining lattice theory, in the form of formal concept analysis, and entropy triangles, obtained from information theory, we explain from first principles the fundamental issues of multilabel datasets such as the dependencies of the labels, their imbalances, or the effects of the presence of hapaxes. This allows us to provide guidelines for resampling and new data collection and their relationship with broad modelling approaches. We have empirically validated our framework using 56 open datasets, challenging previous characterizations that prove that our formalization brings useful insights into the task of multilabel classification. Further work will consider the extension of this formalization to understand the relationship between the data sources, the classification methods, and ways to assess their performance.

Keywords

• multilabel classification
• multilabel datasets
• information sources
• formal concept analysis
• entropy balances
• meta-analysis