On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification

• URL: http://arxiv.org/abs/2207.13186v1
• Date: Tue, 26 Jul 2022 21:23:23 GMT
• Title: On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification
• Authors: Erik Schultheis, Marek Wydmuch, Rohit Babbar, Krzysztof Dembczy\'nski
• Abstract summary: The propensity model introduced by Jain et al. 2016 has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC) We exhaustively discuss the flaws of the propensity-based approach, and present several recipes, some of them related to solutions used in search engines and recommender systems.
• Score: 5.247557449370602
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The propensity model introduced by Jain et al. 2016 has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC). In this paper, we critically revise this approach showing that despite its theoretical soundness, its application in contemporary XMLC works is debatable. We exhaustively discuss the flaws of the propensity-based approach, and present several recipes, some of them related to solutions used in search engines and recommender systems, that we believe constitute promising alternatives to be followed in XMLC.

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