A General Two-stage Multi-label Ranking Framework

In this paper we develop and study solutions for the multi-label ranking (MLR) problem. Briefly, goal of is not only to assign a set relevant labels data instance but also rank according their importance. To do so propose two-stage model that consists of: (1) classification first selects an unordered instance, and, (2) label ordering orders selected post-hoc in order The advantage such it can represent both dependencies among labels, as well as, We evaluate performance our framework on simulated real-world datasets show its improved compared existing multiple-label solutions.