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license](https://img.shields.io/badge/license-apache-green.svg?style=flat)](https://www.apache.org/licenses/LICENSE-2.0)\n[![Version](https://img.shields.io/badge/version-0.0.1-red.svg?style=flat)]()\n\u003c!---\n[![Travis Status]()]()\n[![Jenkins Status]()]()\n[![Coverage Status]()]()\n---\u003e\n## Introduction\n\nA set of tools for building high diversity ensembles.\n\n* a set of quantitative metrics for assessing the quality of ensembles;\n* a suite of baseline diversity metrics and optimized diversity metrics for identifying and selecting ensembles with high diversity and high quality;\n* representative ensemble consensus methods: soft voting (model averaging), majority voting, plurality voting and boosting voting.\n\nCogMI 2020 Presentation Video: https://youtu.be/ErZj_OxyYxc\n\nIf you find this work useful in your research, please cite the following papers:\n\n**Bibtex**:\n```bibtex\n@INPROCEEDINGS{ensemblebench,\n    author={Y. {Wu} and L. {Liu} and Z. {Xie} and J. {Bae} and K. -H. {Chow} and W. {Wei}},\n    booktitle={2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)},\n    title={Promoting High Diversity Ensemble Learning with EnsembleBench},\n    year={2020},\n    volume={},\n    number={},\n    pages={208-217},\n    doi={10.1109/CogMI50398.2020.00034}\n}\n@INPROCEEDINGS{dp-ensemble,\n    author={Wu, Yanzhao and Liu, Ling and Xie, Zhongwei and Chow, Ka-Ho and Wei, Wenqi},\n    booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, \n    title={Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics}, \n    year={2021},\n    volume={},\n    number={},\n    pages={16464-16472},\n    doi={10.1109/CVPR46437.2021.01620}\n}\n@INPROCEEDINGS{hq-ensemble,\n    author={Wu, Yanzhao and Liu, Ling},\n    booktitle={2021 IEEE International Conference on Data Mining (ICDM)}, \n    title={Boosting Deep Ensemble Performance with Hierarchical Pruning}, \n    year={2021},\n    volume={},\n    number={},\n    pages={1433-1438},\n    doi={10.1109/ICDM51629.2021.00184}\n}\n```\n\n## Instructions\n\n\n### Installation\n\n1. It is recommended to clone this git repo and refer to the demo folder for building your own projects using EnsembleBench.\n\n       git clone https://github.com/git-disl/EnsembleBench.git\n    \n2. Initialize the environmental variables:\n\n       source env.sh\n\n3. Install the Python dependencies.\n\n4. Run the demos under the demo folder.\n\n\nIf you would like to simply use some functions provided by EnsembleBench, you may install it using the following command.\n    \n    pip install EnsembleBench\n\n\n\n## Supported Platforms\n\nThe source codes have been tested on Ubuntu 16.04 and Ubuntu 20.04.\n\n\n\n## Development / Contributing\n\n\n## Issues\n\n\n## Status\n\n\n## Contributors\n\nSee the [people page](https://github.com/git-disl/EnsembleBench/graphs/contributors) for the full listing of contributors.\n\n## License\n\nCopyright (c) 20XX-20XX [Georgia Tech DiSL](https://github.com/git-disl)  \nLicensed under the [Apache License](LICENSE).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgit-disl%2Fensemblebench","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgit-disl%2Fensemblebench","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgit-disl%2Fensemblebench/lists"}