The csmcal from ej0cl6

csmcal's Introduction

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