Bonsai: diverse and shallow trees for extreme multi-label classification

Sujay Khandagale, Han Xiao, Rohit Babbar

Research output: Contribution to journalArticlepeer-review

60 Citations (SciVal)

Abstract

Extreme multi-label classification (XMC) refers to supervised multi-label learning involving hundreds of thousands or even millions of labels.In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees.We show three concrete realizations of this label representation space including: (i) the input space which is spanned by the input features, (ii) the output space spanned by label vectors based on their co-occurrence with other labels, and (iii) the joint space by combining the input and output representations. Furthermore, the constraint-free multi-way partitions learnt iteratively in these spaces lead to shallow trees.By combining the effect of shallow trees and generalized label representation, Bonsai achieves the best of both worlds—fast training which is comparable to state-of-the-art tree-based methods in XMC, and much better prediction accuracy, particularly on tail-labels. On a benchmark Amazon-3M dataset with 3 million labels, Bonsai outperforms a state-of-the-art one-vs-rest method in terms of prediction accuracy, while being approximately 200 times faster to train. The code for Bonsai is available at https://github.com/xmc-aalto/bonsai.

Original languageEnglish
Pages (from-to)2099-2119
Number of pages21
JournalMachine Learning
Volume109
Issue number11
DOIs
Publication statusPublished - 23 Aug 2020

Keywords

  • Extreme multi-label classification
  • Large label space
  • Large-scale multi-label classification

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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