Cost-Sensitive Multi-Label Classification
Clone or download
ej0cl6
Latest commit 369c2bb Oct 29, 2017
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
models clems K -> z_dim Oct 29, 2017
scene version 1.0 Oct 27, 2017
.gitignore version 1.0 Oct 27, 2017
README.md Update README.md Oct 28, 2017
criteria.py version 1.0 Oct 27, 2017
demo.py tree num Oct 27, 2017

README.md

Cost-Sensitive Multi-Label Classification

Python implementation of our paper Cost-Sensitive Label Embedding for Multi-Label Classification and related algorithms, including:

  • Cost-Sensitive Label Embedding with Multidimensional Scaling (CLEMS)
  • Condensed Filter Tree (CFT)
  • Probabilistic Classifier Chains (PCC)
  • Classifier Chains (CC)
  • Binary Relevance (BR)

If you find our paper or implementation is useful in your research, please consider citing our paper for CLEMS and the references below for other algorithms.

@article{Huang2017clems,
    author    = {Kuan-Hao Huang and
                 Hsuan-Tien Lin},
    title     = {Cost-sensitive label embedding for multi-label classification},
    journal   = {Machine Learning},
    volume    = {106},
    number    = {9-10},
    pages     = {1725--1746},
    year      = {2017},
}

Prerequisites

  • Python 2.7.12
  • NumPy 1.13.3
  • scikit-learn 0.17

Usage

$ python demo.py

Dataset

  • scene (downloaded from Mulan)

Evaluation Criteria

  • Hamming loss
  • Rank loss
  • F1 score
  • Accuracy score

Result

============================================================
algorithm  hamming_loss  rank_loss  f1_score  accuracy_score
============================================================
       BR        0.0907     1.1844    0.5742          0.5627
       CC        0.0880     1.1424    0.5947          0.5851
      PCC        0.0900     0.6898    0.7460          0.6909
      CFT        0.0867     0.9460    0.6478          0.6267
    CLEMS        0.0825     0.6553    0.7690          0.7600
============================================================

Reference

  • Grigorios Tsoumakas and Ioannis Katakis. Multi-Label Classification: An Overview. International Journal of Data Warehousing and Mining, 2007.

  • Jesse Read, Bernhard, Pfahringer, Geoff Holmes, and Eibe Frank. Classifier chains for multi-label classification. Machine Learning, 2011

  • Krzysztof Dembczynski, Weiwei Cheng, and Eyke Hullermeier. Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains. ICML, 2012.

  • Chun-Liang Li and Hsuan-Tien Lin. Condensed Filter Tree for Cost-Sensitive Multi-Label Classification. ICML, 2014.

  • Kuan-Hao Huang and Hsuan-Tien Lin. Cost-Sensitive Label Embedding for Multi-Label Classification. Machine Learning, 2017

Author

Kuan-Hao Huang / @ej0cl6