Cost-Sensitive Multi-Class Active Learning

Python implementation of our paper A Novel Uncertainty Sampling Algorithm for Cost-Sensitive Multiclass Active Learning and related algorithms, including:

• Active Learning with Cost Embedding (ALCE)
• Uncertainty Sampling with Margin (UncertaintyMargin)
• Uncertainty Sampling with Entropy (UncertaintyEntropy)
• Maximum Expected Cost (MEC)
• Cost-Weighted Minimum Margin (CWMM)
• Random Sampling

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

@inproceedings{Huang2016alce,
    author    = {Kuan-Hao Huang and
                 Hsuan-Tien Lin},
    title     = {A Novel Uncertainty Sampling Algorithm for Cost-Sensitive Multiclass Active Learning},
    booktitle = {Proceedings of the IEEE International Conference on Data Mining (ICDM)},
    pages     = {925--930},
    year      = {2016},
}

Prerequisites

• Python 2.7.10
• NumPy 1.9.2
• scikit-learn 0.19.1
• Matplotlib 1.3.1

Usage

$ python demo.py

Dataset

• vehicle (downloaded from LIBSVM Data)

Result

Reference

• Simon Tong and Daphne Koller. Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research, 2001

• Feng Jing, Mingjing Li, HongJiang Zhang, and Bo Zhang. Entropy-Based Active Learning with Support Vector Machines for Content-Based Image Retrieval. ICME, 2004

• Po-Lung Chen and Hsuan-Tien Lin. Active Learning for Multiclass Cost-Sensitive Classification Using Probabilistic Models. TAAI, 2013

• Kuan-Hao Huang and Hsuan-Tien Lin. A Novel Uncertainty Sampling Algorithm for Cost-Sensitive Multiclass Active Learning. ICDM, 2016

Author

Kuan-Hao Huang / @ej0cl6