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Introduction

Multiclass classification is an important problem in machine learning. It can be used in a variety of applications, such as organizing documents to different categories automatically. Multi-label classification is an extension of multi-class classification — the former allows a set of labels to be associated with an instance while the latter allows only one. For instance, a document may belong to both the “politics” and “health” class if it is about the National Health Insurance. Many other similar applications arise in domains like text mining, vision, or bio-informatics.

Our Related Work

paper

  • Chun-Sung Ferng and Hsuan-Tien Lin. Multi-label Classification Using Error-correcting Codes of Hard or Soft Bits. IEEE Transactions on Neural Networks and Learning Systems, 2013 link. A shorter version appeared in ACML 2011 link, with code: ml_ecc.20130515.tgz
  • Yao-Nan Chen and Hsuan-Tien Lin. Feature-aware Label Space Dimension Reduction for Multi-label Classification, NIPS 2012. link
  • Farbound Tai and Hsuan-Tien Lin. Multi-label Classification with Principal Label Space Transformation. Neural Computation, 24(9), 2508-2542. link. A preliminary version appeared in MLD Workshop @ ICML '10 link, with code: snapshot on 2011/01/23
  • Chen-Wei Hung and Hsuan-Tien Lin. Multi-label Active Learning with Auxiliary Learner, ACML 2011. link

thesis

  • Yao-Nan Chen. Feature-aware Label Space Dimension Reduction for Multi-label Classification. Master's Thesis, 2012.
  • Chun-Sung Ferng. Multi-label Classification with Hard-/soft-decoded Error-correcting Codes. Master's Thesis, 2012.
  • Chen-Wei Hung. Multi-label Active Learning with Auxiliary Learner. Master's Thesis, 2011.
public/multi-label_classification.txt · Last modified: 2013/07/07 05:57 by htlin