Three-way decision in machine learning tasks: a systematic review

Abstract

AbstractIn this article, we survey the applications of Three-way decision theory (TWD) in machine learning (ML), focusing in particular on four tasks: weakly supervised learning and multi-source data management, missing data management, uncertainty quantification in classification, and uncertainty quantification in clustering. For each of these four tasks we present the results of a systematic review of the literature, by which we report on the main characteristics of the current state of the art, as well as on the quality of reporting and reproducibility level of the works found in the literature. To this aim, we discuss the main benefits, limitations and issues found in the reviewed articles, and we give clear indications and directions for quality improvement that are informed by validation, reporting, and reproducibility standards, guidelines and best practice that have recently emerged in the ML field. Finally, we discuss about the more promising and relevant directions for future research in regard to TWD.