A Taxonomy of Label Ranking Algorithms

Abstract

The problem of learning label rankings is receiving increasing attention from machine learning and data mining community. Its goal is to learn a mapping from instances to rankings over a finite number of labels. In this paper, we devote to giving an overview of the state-of-the-art in the area of label ranking, and providing a basic taxonomy of the label ranking algorithms. Specifically, we classify these label ranking algorithms into four categories, namely decomposition methods, probabilistic methods, similarity-based methods, and other methods. We pay particular attention to the latest advances in each. Also, we discuss their strengths and weaknesses, and highlight some interesting challenges that remain to be solved.