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The Ethereal
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification
July 26, 2022 ยท Declared Dead ยท ๐ Knowledge Discovery and Data Mining
"No code URL or promise found in abstract"
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Authors
Erik Schultheis, Marek Wydmuch, Rohit Babbar, Krzysztof Dembczyลski
arXiv ID
2207.13186
Category
cs.LG: Machine Learning
Cross-listed
cs.IR
Citations
32
Venue
Knowledge Discovery and Data Mining
Last Checked
4 months ago
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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