Getting Start

ALCS: A A clustering-based active learning method to query informative and representative samples

Here we prepared two versions of ALCS framework:

1. main_final_active2.py: implmentation of ALCS framework without the diversity exploration procedure.
2. main_final_active2.py: implementation of ALCS framework with the FPN-based diversity exploration procedure.

For both of them, you can change the label ratio by variable "label_ratiovalues". Also, you can substitute the fps_clustering function with any other clustering approach as long as it returns the following output variables:

cluster centers
cluster labels
data samples
class labels

Example Usage:

To modify the input dataset, you can change the Input function in the code as follows:

`sample = pd.read_csv('path/to/datasets)`

Also, an example jupternotebook file "example_alcs.ipynb" is added for users to directly use the code on google colab.

Dependencies:

Please install the following package before running the python code.

• Numpy
• Pandas
• Scipy
• Scikit-learn

Citation format:

For any use of this python code, please cite the following paper:

• Yan, Xuyang, Shabnam Nazmi, Biniam Gebru, Mohd Anwar, Abdollah Homaifar, Mrinmoy Sarkar, and Kishor Datta Gupta. "A clustering-based active learning method to query informative and representative samples." Applied Intelligence (2022): 1-18.
• Yan, X., Nazmi, S., Gebru, B., Anwar, M., Homaifar, A., Sarkar, M., & Gupta, K. D. (2022). Mitigating shortage of labeled data using clustering-based active learning with diversity exploration. arXiv preprint arXiv:2207.02964. Note: the implementation of ALCS without diversity exploration will be updated soon to make the code easier to read and follow.

The bib style can be found below:

• @article{yan2022clustering, title={A clustering-based active learning method to query informative and representative samples}, author={Yan, Xuyang and Nazmi, Shabnam and Gebru, Biniam and Anwar, Mohd and Homaifar, Abdollah and Sarkar, Mrinmoy and Gupta, Kishor Datta}, journal={Applied Intelligence}, volume={52}, number={11}, pages={13250--13267}, year={2022}, publisher={Springer} }
• @article{yan2022mitigating, title={Mitigating shortage of labeled data using clustering-based active learning with diversity exploration}, author={Yan, Xuyang and Nazmi, Shabnam and Gebru, Biniam and Anwar, Mohd and Homaifar, Abdollah and Sarkar, Mrinmoy and Gupta, Kishor Datta}, journal={arXiv preprint arXiv:2207.02964}, year={2022} }