`

Timezone: »

 
Poster
A no-regret generalization of hierarchical softmax to extreme multi-label classification
Marek Wydmuch · Kalina Jasinska-Kobus · Mikhail Kuznetsov · Róbert Busa-Fekete · Krzysztof Dembczynski

Thu Dec 06 07:45 AM -- 09:45 AM (PST) @ Room 517 AB #137
in Thu Poster Session A »
Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels. Large label spaces can be efficiently handled by organizing labels as a tree, like in the hierarchical softmax (HSM) approach commonly used for multi-class problems. In this paper, we investigate probabilistic label trees (PLTs) that have been recently devised for tackling XMLC problems. We show that PLTs are a no-regret multi-label generalization of HSM when precision@$k$ is used as a model evaluation metric. Critically, we prove that pick-one-label heuristic---a reduction technique from multi-label to multi-class that is routinely used along with HSM---is not consistent in general. We also show that our implementation of PLTs, referred to as extremeText (XT), obtains significantly better results than HSM with the pick-one-label heuristic and XML-CNN, a deep network specifically designed for XMLC problems. Moreover, XT is competitive to many state-of-the-art approaches in terms of statistical performance, model size and prediction time which makes it amenable to deploy in an online system.
Keywords: Structured Prediction · Classification
[ Paper]  [ Poster] 

Author Information

Marek Wydmuch (Poznan University of Technology)
Kalina Jasinska-Kobus (Poznan University of Technology, Allegro.pl)
Mikhail Kuznetsov (Yahoo! Research)
Róbert Busa-Fekete (Yahoo! Research)
Krzysztof Dembczynski (Poznan University of Technology)

More from the Same Authors