{"id":23092560,"url":"https://github.com/daochenzha/autosmote","last_synced_at":"2025-08-18T15:10:48.528Z","repository":{"id":133069799,"uuid":"456350341","full_name":"daochenzha/autosmote","owner":"daochenzha","description":"[CIKM 2022] Towards Automated Over-Sampling for Imbalanced Classification","archived":false,"fork":false,"pushed_at":"2023-03-20T06:49:52.000Z","size":184,"stargazers_count":10,"open_issues_count":1,"forks_count":1,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-04T10:11:15.840Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/daochenzha.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-02-07T03:54:57.000Z","updated_at":"2024-02-01T10:24:14.000Z","dependencies_parsed_at":null,"dependency_job_id":"a4015e04-0b48-4507-a28d-8197c915b285","html_url":"https://github.com/daochenzha/autosmote","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/daochenzha/autosmote","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daochenzha%2Fautosmote","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daochenzha%2Fautosmote/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daochenzha%2Fautosmote/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daochenzha%2Fautosmote/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/daochenzha","download_url":"https://codeload.github.com/daochenzha/autosmote/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daochenzha%2Fautosmote/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270701573,"owners_count":24630868,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-16T02:00:11.002Z","response_time":91,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-12-16T21:34:35.619Z","updated_at":"2025-08-18T15:10:48.521Z","avatar_url":"https://github.com/daochenzha.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# [CIKM 2022] Towards Automated Over-Sampling for Imbalanced Classification\nThis is the implementation for the paper [Towards Automated Over-Sampling for Imbalanced Classification](https://arxiv.org/abs/2208.12433). We propose AutoSMOTE, an automated over-sampling algorithm for imbalanced classification. It jointly optimize different levels of decisions with deep hierarchical reinforcement learning. Please refer the paper for more details.\n\n:loudspeaker: Do you want to learn more about oversampling or data augmentation? Please check out our [data-centric AI survey](https://arxiv.org/abs/2303.10158) and [data-centric AI resources](https://github.com/daochenzha/data-centric-AI)!\n\n\u003cimg width=\"800\" src=\"./imgs/overview.png\" alt=\"overview\" /\u003e\n\n## Cite this Work\nIf you find this project helpful, please cite\n```bibtex\n@inproceedings{zha2022automated,\n      title={Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning}, \n      author={Daochen Zha and Kwei-Herng Lai and Qiaoyu Tan and Sirui Ding and Na Zou and Xia Hu},\n      booktitle={CIKM},\n      year={2022},\n}\n```\n\n# Installation\nMake sure that you have Python 3.6+ installed. Install with\n```\npip3 install -r requirements.txt\npip3 install -e .\n```\n\n# Datasets\nYou don't need to mannually download datasets. Just pass the dataset name, and it will be automatically downloaded.\n\n# Quick Start\nTrain on the Mozilla4 dataset with undersampling ratio of 100 and SVM as the base classifier:\n```\npython3 train.py\n```\n\n# Important Arguments\nYou can run AutoSMOTE under different configurations. Some important arguments are listed below.\n*   `--dataset`: which dataset to use\n*   `--clf`: which base classifeir to use\n*   `--metric`: which metric to use\n*   `--device`: by default it trains with GPU. Train with CPU by passing `cpu`\n*   `--total_steps`: search budget\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdaochenzha%2Fautosmote","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdaochenzha%2Fautosmote","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdaochenzha%2Fautosmote/lists"}