Multi-label Ranking: Mining Multi-label and Label Ranking Data

Abstract

We survey multi-label ranking tasks, specifically multi-label classification and label ranking classification. We highlight the unique challenges, and recategorize the methods, as they no longer fit into the traditional categories of transformation and adaptation. We survey developments in the last demi-decade, with a special focus on state-of-the-art methods in deep learning multi-label mining, extreme multi-label classification and label ranking. We conclude by offering a few future research directions.

Heuristic Search for Rank Aggregation with Application to Label Ranking

“…Rank aggregation is a classical problem in voting theory, where each voter provides a preference ranking on a set of alternatives, and the system aggregates these rankings into a single consensus preference order to rank the alternatives. Rank aggregation plays a critical role in a variety of applications such as collaborative filtering [1], [2], multiagent planning [3], information retrieval [4], and label ranking [5]- [7]. As a result, this problem has been widely studied, particularly in social choice theory and artificial intelligence.…”