An easy-to-hard learning paradigm for multiple classes and multiple labels

Weiwei Liu, Ivor W. Tsang, Klaus Robert Müller

Research output: Contribution to journalArticlepeer-review

88 Citations (Scopus)


Many applications, such as human action recognition and object detection, can be formulated as a multiclass classification problem. One-vs-rest (OVR) is one of the most widely used approaches for multiclass classification due to its simplicity and excellent performance. However, many confusing classes in such applications will degrade its results. For example, hand clap and boxing are two confusing actions. Hand clap is easily misclassified as boxing, and vice versa. Therefore, precisely classifying confusing classes remains a challenging task. To obtain better performance for multiclass classifications that have confusing classes, we first develop a classifier chain model for multiclass classification (CCMC) to transfer class information between classifiers. Then, based on an analysis of our proposed model, we propose an easy-to-hard learning paradigm for multiclass classification to automatically identify easy and hard classes and then use the predictions from simpler classes to help solve harder classes. Similar to CCMC, the classifier chain (CC) model is also proposed by Read et al. (2009) to capture the label dependency for multi-label classification. However, CC does not consider the order of difficulty of the labels and achieves degenerated performance when there are many confusing labels. Therefore, it is non-trivial to learn the appropriate label order for CC. Motivated by our analysis for CCMC, we also propose the easy-to-hard learning paradigm for multi-label classi cation to automatically identify easy and hard labels, and then use the predictions from simpler labels to help solve harder labels. We also demonstrate that our proposed strategy can be successfully applied to a wide range of applications, such as ordinal classi cation and relationship prediction. Extensive empirical studies validate our analysis and the e-ectiveness of our proposed easy-to-hard learning strategies.

Original languageEnglish
Pages (from-to)1-38
Number of pages38
JournalJournal of Machine Learning Research
Publication statusPublished - 2017 Sept 1

Bibliographical note

Funding Information:
We would like to thank the action editor and reviewers for their valuable comments and constructive suggestions on our paper. This project was partially supported by the ARC Future Fellowship FT130100746, ARC grant LP150100671, DP170101628, DP150102728, DP150103071, NSFC 61232006 and NSFC 61672235. Klaus-Robert Müller acknowledges partial funding from DFG & BMBF and was also supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451).

Publisher Copyright:
© 2017 Weiwei Liu, Ivor W. Tsang and Klaus-Robert Müller.


  • Classier Chain
  • Easyto-hard Learning Paradigm
  • Multi-label Classication
  • Multiclass Classication

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence


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