Open AccessA General Two-stage Multi-label Ranking Framework
Author(s) -
Yi Xue,
Miloš Hauskrecht
Publication year - 2021
Publication title -
proceedings of the ... international florida artificial intelligence research society conference
Language(s) - English
Resource type - Journals
eISSN - 2334-0762
pISSN - 2334-0754
DOI - 10.32473/flairs.v34i1.128505
Subject(s) - ranking (information retrieval) , computer science , set (abstract data type) , rank (graph theory) , ranking svm , multi label classification , data mining , artificial intelligence , machine learning , learning to rank , data set , information retrieval , mathematics , combinatorics , programming language
In this paper we develop and study solutions for the multi-label ranking (MLR) problem. Briefly, the goal of multi-label ranking is not only to assign a set of relevant labels to a data instance but also to rank the labels according to their importance. To do so we propose a two-stage model that consists of: (1) a multi-label classification model that first selects an unordered set of labels for a data instance, and, (2) a label ordering model that orders the selected labels post-hoc in order of their importance. The advantage of such a model is that it can represent both the dependencies among labels, as well as, their importance. We evaluate the performance of our framework on both simulated and real-world datasets and show its improved performance compared to the existing multiple-label ranking solutions.