Multi-dimensional classification: paradigm, algorithms and beyond

Abstract

Multi-dimensional classification (MDC) aims at learning from objects where each of them is represented by a single instance while associated with multiple class variables. In recent years, this practical learning paradigm has attracted increasing attentions in machine learning community. In this paper, a timely review on this topic is provided with emphasis on representative algorithms. Firstly, the MDC learning framework, commonly used evaluation metrics and publicly available MDC datasets are given. Then, eight state-of-the-art MDC algorithms are scrutinized as the representatives of three categories. After that, several related learning settings are briefly summarized. Finally, this paper is concluded with discussing some open problems to be studied in the future.