{"id":15629827,"url":"https://github.com/zhiningliu1998/self-paced-ensemble","last_synced_at":"2025-04-05T04:10:22.699Z","repository":{"id":45066148,"uuid":"206526243","full_name":"ZhiningLiu1998/self-paced-ensemble","owner":"ZhiningLiu1998","description":"[ICDE'20] ⚖️ A general, efficient ensemble framework for imbalanced classification. | 泛用，高效，鲁棒的类别不平衡学习框架","archived":false,"fork":false,"pushed_at":"2024-02-05T22:10:40.000Z","size":1514,"stargazers_count":257,"open_issues_count":1,"forks_count":50,"subscribers_count":6,"default_branch":"master","last_synced_at":"2025-03-29T03:09:38.831Z","etag":null,"topics":["class-imbalance","classification","ensemble","ensemble-learning","ensemble-methods","ensemble-model","imbalance-classification","imbalanced-data","imbalanced-learn","imbalanced-learning","machine-learning","pypi","python3"],"latest_commit_sha":null,"homepage":"https://arxiv.org/abs/1909.03500v3","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ZhiningLiu1998.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2019-09-05T09:31:21.000Z","updated_at":"2025-03-27T02:28:07.000Z","dependencies_parsed_at":"2023-01-20T05:29:06.276Z","dependency_job_id":"7d148c19-1abb-4855-818f-ccb3c051bac4","html_url":"https://github.com/ZhiningLiu1998/self-paced-ensemble","commit_stats":{"total_commits":61,"total_committers":5,"mean_commits":12.2,"dds":0.360655737704918,"last_synced_commit":"532c0c60929d458426d97fac80ae93bf46d15ae5"},"previous_names":[],"tags_count":4,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZhiningLiu1998%2Fself-paced-ensemble","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZhiningLiu1998%2Fself-paced-ensemble/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZhiningLiu1998%2Fself-paced-ensemble/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZhiningLiu1998%2Fself-paced-ensemble/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ZhiningLiu1998","download_url":"https://codeload.github.com/ZhiningLiu1998/self-paced-ensemble/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247284949,"owners_count":20913704,"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","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":["class-imbalance","classification","ensemble","ensemble-learning","ensemble-methods","ensemble-model","imbalance-classification","imbalanced-data","imbalanced-learn","imbalanced-learning","machine-learning","pypi","python3"],"created_at":"2024-10-03T10:29:07.807Z","updated_at":"2025-04-05T04:10:22.668Z","avatar_url":"https://github.com/ZhiningLiu1998.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003c!-- \u003ch1 align=\"center\"\u003e Self-paced Ensemble \u003c/h1\u003e --\u003e\n\n![](https://raw.githubusercontent.com/ZhiningLiu1998/figures/master/spe/spe_header.png)\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/ZhiningLiu1998/self-paced-ensemble\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/ZhiningLiu1998-SPE-orange\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://github.com/ZhiningLiu1998/self-paced-ensemble/stargazers\"\u003e\n    \u003cimg src=\"https://img.shields.io/github/stars/ZhiningLiu1998/self-paced-ensemble\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://github.com/ZhiningLiu1998/self-paced-ensemble/network/members\"\u003e\n    \u003cimg src=\"https://img.shields.io/github/forks/ZhiningLiu1998/self-paced-ensemble\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://github.com/ZhiningLiu1998/self-paced-ensemble/issues\"\u003e\n    \u003cimg src=\"https://img.shields.io/github/issues/ZhiningLiu1998/self-paced-ensemble\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://github.com/ZhiningLiu1998/self-paced-ensemble/blob/master/LICENSE\"\u003e\n    \u003cimg src=\"https://img.shields.io/github/license/ZhiningLiu1998/self-paced-ensemble\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://pypi.org/project/self-paced-ensemble/\"\u003e\n    \u003cimg src=\"https://badge.fury.io/py/self-paced-ensemble.svg\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://www.python.org/\"\u003e\n    \u003cimg src=\"https://img.shields.io/pypi/pyversions/self-paced-ensemble.svg\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://github.com/ZhiningLiu1998/self-paced-ensemble/graphs/traffic\"\u003e\n    \u003cimg src=\"https://visitor-badge.glitch.me/badge?page_id=ZhiningLiu1998.self-paced-ensemble\"\u003e\n  \u003c/a\u003e\n  \u003c!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section --\u003e\n\u003ca href=\"https://github.com/ZhiningLiu1998/self-paced-ensemble#contributors-\"\u003e\u003cimg src=\"https://img.shields.io/badge/all_contributors-7-orange.svg\"\u003e\u003c/a\u003e\n\u003c!-- ALL-CONTRIBUTORS-BADGE:END --\u003e\n  \u003ca href=\"https://pepy.tech/project/self-paced-ensemble\"\u003e\n    \u003cimg src=\"https://pepy.tech/badge/self-paced-ensemble\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://pepy.tech/project/self-paced-ensemble\"\u003e\n    \u003cimg src=\"https://pepy.tech/badge/self-paced-ensemble/month\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\n\u003ch3 align=\"center\"\u003e Self-paced Ensemble for Highly Imbalanced Massive Data Classification\n(ICDE 2020)\n\u003c/h3\u003e\n\n\u003ch3 align=\"center\"\u003e\nLinks: \n\u003ca href=\"https://conferences.computer.org/icde/2020/pdfs/ICDE2020-5acyuqhpJ6L9P042wmjY1p/290300a841/290300a841.pdf\"\u003ePaper\u003c/a\u003e | \n\u003ca href=\"https://zhiningliu.com/files/ICDE_2020_SPE_slides.pdf\"\u003eSlides\u003c/a\u003e | \n\u003ca href=\"https://www.bilibili.com/video/BV1Fg411L7gk\"\u003eVideo\u003c/a\u003e | \n\u003ca href=\"https://arxiv.org/abs/1909.03500v3\"\u003earXiv\u003c/a\u003e | \n\u003ca href=\"https://pypi.org/project/self-paced-ensemble\"\u003ePyPI\u003c/a\u003e | \n\u003ca href=\"https://imbalanced-ensemble.readthedocs.io/en/latest/api/ensemble/_autosummary/imbens.ensemble.SelfPacedEnsembleClassifier.html\"\u003eAPI Reference\u003c/a\u003e | \n\u003ca href=\"https://github.com/ZhiningLiu1998/self-paced-ensemble#related-projects\"\u003eRelated Projects\u003c/a\u003e |\n\u003ca href=\"https://zhuanlan.zhihu.com/p/86891438\"\u003eZhihu/知乎\u003c/a\u003e\n\u003c/h3\u003e\n\n\n**Self-paced Ensemble (SPE) is an ensemble learning framework for massive highly imbalanced classification. It is an easy-to-use solution to class-imbalanced problems, features outstanding computing efficiency, good performance, and wide compatibility with different learning models. This SPE implementation supports multi-class classification.**\n\n\u003ctable\u003e\u003ctr\u003e\u003ctd bgcolor=MistyRose align=\"center\"\u003e\u003cstrong\u003e\n\u003cfont color='red'\u003eNote: \u003c/font\u003e \n\u003cfont color=Navy\u003e \nSPE is now a part of \u003ca href=\"https://github.com/ZhiningLiu1998/imbalanced-ensemble\"\u003e imbalanced-ensemble \u003c/a\u003e [\u003ca href=\"https://imbalanced-ensemble.readthedocs.io/en/latest/\"\u003eDoc\u003c/a\u003e, \u003ca href=\"https://pypi.org/project/imbalanced-ensemble/\"\u003ePyPI\u003c/a\u003e]. Try it for more methods and advanced features!\n\u003c/font\u003e\n\u003c/strong\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/table\u003e\n\n## Cite Us\n\n**If you find this repository helpful in your work or research, we would greatly appreciate citations to the following [paper](https://arxiv.org/pdf/1909.03500v3.pdf):**\n\n```bib\n@inproceedings{liu2020self-paced-ensemble,\n    title={Self-paced Ensemble for Highly Imbalanced Massive Data Classification},\n    author={Liu, Zhining and Cao, Wei and Gao, Zhifeng and Bian, Jiang and Chen, Hechang and Chang, Yi and Liu, Tie-Yan},\n    booktitle={2020 IEEE 36th International Conference on Data Engineering (ICDE)},\n    pages={841--852},\n    year={2020},\n    organization={IEEE}\n}\n```\n\n## Installation\n\nIt is recommended to use **pip** for installation.  \nPlease make sure the **latest version** is installed to avoid potential problems:\n```shell\n$ pip install self-paced-ensemble            # normal install\n$ pip install --upgrade self-paced-ensemble  # update if needed\n```\n\nOr you can install SPE by clone this repository:\n```shell\n$ git clone https://github.com/ZhiningLiu1998/self-paced-ensemble.git\n$ cd self-paced-ensemble\n$ python setup.py install\n```\n\nFollowing dependencies are required:\n- [python](https://www.python.org/) (\u003e=3.6)\n- [numpy](https://numpy.org/) (\u003e=1.13.3)\n- [scipy](https://www.scipy.org/) (\u003e=0.19.1)\n- [joblib](https://pypi.org/project/joblib/) (\u003e=0.11)\n- [scikit-learn](https://scikit-learn.org/stable/) (\u003e=0.24)\n- [imblearn](https://pypi.org/project/imblearn/) (\u003e=0.7.0)\n- [imbalanced-ensemble](https://pypi.org/project/imbalanced-ensemble/) (\u003e=0.1.3)\n\n## Table of Contents\n\n- [Cite Us](#cite-us)\n- [Installation](#installation)\n- [Table of Contents](#table-of-contents)\n- [Background](#background)\n- [Documentation](#documentation)\n- [Examples](#examples)\n  - [**API demo**](#api-demo)\n  - [**Advanced usage example**](#advanced-usage-example)\n  - [Save \\\u0026 Load model](#save--load-model)\n  - [**Compare SPE with other methods**](#compare-spe-with-other-methods)\n- [Results](#results)\n- [Miscellaneous](#miscellaneous)\n- [References](#references)\n- [Related Projects](#related-projects)\n- [Contributors ✨](#contributors-)\n\n## Background\n\nSPE performs strictly balanced under-sampling in each iteration and is therefore very *computationally efficient*. In addition, SPE does not rely on calculating the distance between samples to perform resampling. It can be easily applied to datasets that lack well-defined distance metrics (e.g. with categorical features / missing values) without any modification. Moreover, as a *generic ensemble framework*, our methods can be easily adapted to most of the existing learning methods (e.g., C4.5, SVM, GBDT, and Neural Network) to boost their performance on imbalanced data. Compared to existing imbalance learning methods, *SPE works particularly well on datasets that are large-scale, noisy, and highly imbalanced (e.g. with imbalance ratio greater than 100:1).* Such kind of data widely exists in real-world industrial applications. The figure below gives an overview of the SPE framework.\n\n![image](https://raw.githubusercontent.com/ZhiningLiu1998/figures/master/spe/framework.png)\n\n## Documentation\n\n**Our SPE implementation can be used much in the same way as the [`sklearn.ensemble`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble) classifiers. Detailed documentation of ``SelfPacedEnsembleClassifier`` can be found [HERE](https://imbalanced-ensemble.readthedocs.io/en/latest/api/ensemble/_autosummary/imbens.ensemble.SelfPacedEnsembleClassifier.html).**\n\n## Examples\n\nYou can check out [**examples using SPE**](https://imbalanced-ensemble.readthedocs.io/en/latest/api/ensemble/_autosummary/imbens.ensemble.SelfPacedEnsembleClassifier.html#examples-using-imbalanced-ensemble-ensemble-selfpacedensembleclassifier) for more comprehensive usage examples.\n\n\n![](https://raw.githubusercontent.com/ZhiningLiu1998/figures/master/imbalanced-ensemble/example_gallery_snapshot.png)\n\n\n### **API demo**\n```python\nfrom self_paced_ensemble import SelfPacedEnsembleClassifier\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.datasets import make_classification\nfrom sklearn.model_selection import train_test_split\n\n# Prepare class-imbalanced train \u0026 test data\nX, y = make_classification(n_classes=2, random_state=42, weights=[0.1, 0.9])\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, test_size=0.5, random_state=42)\n\n# Train an SPE classifier\nclf = SelfPacedEnsembleClassifier(\n        base_estimator=DecisionTreeClassifier(), \n        n_estimators=10,\n    ).fit(X_train, y_train)\n\n# Predict with an SPE classifier\nclf.predict(X_test)\n```\n\n### **Advanced usage example**\n\nPlease see [usage_example.ipynb](https://github.com/ZhiningLiu1998/self-paced-ensemble/blob/master/examples/usage_example.ipynb).\n\n### Save \u0026 Load model\n\nWe recommend to use joblib or pickle for saving and loading SPE models, e.g.,\n```python\nfrom joblib import dump, load\n\n# save the model\ndump(clf, filename='clf.joblib')\n# load the model\nclf = load('clf.joblib')\n```\nYou can also use the alternative APIs provided in SPE:\n```python\nfrom self_paced_ensemble.utils import save_model, load_model\n\n# save the model\nclf.save('clf.joblib')        # option 1\nsave_model(clf, 'clf.joblib') # option 2\n# load the model\nclf = load_model('clf.joblib')\n```\n\n### **Compare SPE with other methods**\n\nPlease see [comparison_example.ipynb](https://github.com/ZhiningLiu1998/self-paced-ensemble/blob/master/examples/comparison_example.ipynb).\n\n## Results\n\nDataset links:\n[Credit Fraud](https://www.kaggle.com/mlg-ulb/creditcardfraud), \n[KDDCUP](https://archive.ics.uci.edu/ml/datasets/kdd+cup+1999+data), \n[Record Linkage](https://archive.ics.uci.edu/ml/datasets/Record+Linkage+Comparison+Patterns), \n[Payment Simulation](https://www.kaggle.com/ealaxi/paysim1).  \n\n![image](https://raw.githubusercontent.com/ZhiningLiu1998/figures/master/spe/statistics.png)  \n\nComparisons of SPE with traditional resampling/ensemble methods in terms of performance \u0026 computational efficiency.\n\n\u003c!-- ![image](https://github.com/ZhiningLiu1998/figures/blob/master/spe/results.png) --\u003e\n\n![image](https://raw.githubusercontent.com/ZhiningLiu1998/figures/master/spe/results_resampling.png)\n\n![image](https://raw.githubusercontent.com/ZhiningLiu1998/figures/master/spe/results_ensemble.png)\n\n![image](https://raw.githubusercontent.com/ZhiningLiu1998/figures/master/spe/results_ensemble_curve.png)\n\n## Miscellaneous\n\n**This repository contains:**\n- Implementation of Self-paced Ensemble\n- Implementation of 5 ensemble-based imbalance learning baselines\n  - `SMOTEBoost` [1]\n  - `SMOTEBagging` [2]\n  - `RUSBoost` [3]\n  - `UnderBagging` [4]\n  - `BalanceCascade` [5]\n- Implementation of resampling based imbalance learning baselines [6]\n- Additional experimental results\n\n**NOTE:** The implementations of other ensemble and resampling methods are based on [imbalanced-ensemble](https://github.com/ZhiningLiu1998/imbalanced-ensemble) and [imbalanced-learn](https://github.com/scikit-learn-contrib/imbalanced-learn).\n\n## References\n\n| #   | Reference                                                                                                                                                                                                                                         |\n| --- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| [1] | N. V. Chawla, A. Lazarevic, L. O. Hall, and K. W. Bowyer, Smoteboost: Improving prediction of the minority class in boosting. in European conference on principles of data mining and knowledge discovery. Springer, 2003, pp. 107–119            |\n| [2] | S. Wang and X. Yao, Diversity analysis on imbalanced data sets by using ensemble models. in 2009 IEEE Symposium on Computational Intelligence and Data Mining. IEEE, 2009, pp. 324–331.                                                           |\n| [3] | C. Seiffert, T. M. Khoshgoftaar, J. Van Hulse, and A. Napolitano, “Rusboost: A hybrid approach to alleviating class imbalance,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, vol. 40, no. 1, pp. 185–197, 2010. |\n| [4] | R. Barandela, R. M. Valdovinos, and J. S. Sanchez, “New applications´ of ensembles of classifiers,” Pattern Analysis \u0026 Applications, vol. 6, no. 3, pp. 245–256, 2003.                                                                            |\n| [5] | X.-Y. Liu, J. Wu, and Z.-H. Zhou, “Exploratory undersampling for class-imbalance learning,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, no. 2, pp. 539–550, 2009.                                          |\n| [6] | Guillaume Lemaître, Fernando Nogueira, and Christos K. Aridas. Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning. Journal of Machine Learning Research, 18(17):1–5, 2017.                         |\n\n## Related Projects\n\n**Check out [Zhining](https://zhiningliu.com/)'s other open-source projects!**  \n\u003ctable style=\"font-size:15px;\"\u003e\n  \u003ctr\u003e\n    \u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/ZhiningLiu1998/imbalanced-ensemble\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/ZhiningLiu1998/figures/master/thumbnails/imbens-thumb.png\" height=\"80px\" alt=\"\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eImbalanced-Ensemble [PythonLib]\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\n      \u003ca href=\"https://github.com/ZhiningLiu1998/imbalanced-ensemble/stargazers\"\u003e\n      \u003cimg alt=\"GitHub stars\" src=\"https://img.shields.io/github/stars/ZhiningLiu1998/imbalanced-ensemble?style=social\"\u003e\n      \u003c/a\u003e\n    \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/ZhiningLiu1998/awesome-imbalanced-learning\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/ZhiningLiu1998/figures/master/thumbnails/awesomeil-thumb.png\" height=\"80px\" alt=\"\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eImbalanced Learning [Awesome]\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\n      \u003ca href=\"https://github.com/ZhiningLiu1998/awesome-imbalanced-learning/stargazers\"\u003e\n      \u003cimg alt=\"GitHub stars\" src=\"https://img.shields.io/github/stars/ZhiningLiu1998/awesome-imbalanced-learning?style=social\"\u003e\n      \u003c/a\u003e\n    \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/ZhiningLiu1998/awesome-awesome-machine-learning\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/ZhiningLiu1998/figures/master/thumbnails/awesomeml-thumb.png\" height=\"80px\" alt=\"\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eMachine Learning [Awesome]\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\n      \u003ca href=\"https://github.com/ZhiningLiu1998/awesome-awesome-machine-learning/stargazers\"\u003e\n      \u003cimg alt=\"GitHub stars\" src=\"https://img.shields.io/github/stars/ZhiningLiu1998/awesome-awesome-machine-learning?style=social\"\u003e\n      \u003c/a\u003e\n    \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/ZhiningLiu1998/mesa\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/ZhiningLiu1998/figures/master/thumbnails/mesa-thumb.png\" height=\"80px\" alt=\"\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eMeta-Sampler [NeurIPS]\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\n      \u003ca href=\"https://github.com/ZhiningLiu1998/mesa/stargazers\"\u003e\n      \u003cimg alt=\"GitHub stars\" src=\"https://img.shields.io/github/stars/ZhiningLiu1998/mesa?style=social\"\u003e\n      \u003c/a\u003e\n    \u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\n## Contributors ✨\n\nThanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):\n\n\u003c!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section --\u003e\n\u003c!-- prettier-ignore-start --\u003e\n\u003c!-- markdownlint-disable --\u003e\n\u003ctable\u003e\n  \u003ctr\u003e\n    \u003ctd align=\"center\"\u003e\u003ca href=\"http://zhiningliu.com\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/26108487?v=4?s=100\" width=\"100px;\" alt=\"\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eZhining Liu\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/ZhiningLiu1998/self-paced-ensemble/commits?author=ZhiningLiu1998\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"https://github.com/ZhiningLiu1998/self-paced-ensemble/commits?author=ZhiningLiu1998\" title=\"Documentation\"\u003e📖\u003c/a\u003e \u003ca href=\"#example-ZhiningLiu1998\" title=\"Examples\"\u003e💡\u003c/a\u003e\u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\u003ca href=\"https://yumingfu.space/\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/23732534?v=4?s=100\" width=\"100px;\" alt=\"\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eYuming Fu\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/ZhiningLiu1998/self-paced-ensemble/commits?author=rudolffu\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"https://github.com/ZhiningLiu1998/self-paced-ensemble/issues?q=author%3Arudolffu\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e\u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\u003ca href=\"https://thul.io\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/95307?v=4?s=100\" width=\"100px;\" alt=\"\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eThúlio Costa\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/ZhiningLiu1998/self-paced-ensemble/commits?author=thulio\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"https://github.com/ZhiningLiu1998/self-paced-ensemble/issues?q=author%3Athulio\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e\u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/jerrylususu\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/17522475?v=4?s=100\" width=\"100px;\" alt=\"\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eNeko Null\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#maintenance-jerrylususu\" title=\"Maintenance\"\u003e🚧\u003c/a\u003e\u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/lirenjieArthur\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/31763604?v=4?s=100\" width=\"100px;\" alt=\"\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003elirenjieArthur\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/ZhiningLiu1998/self-paced-ensemble/issues?q=author%3AlirenjieArthur\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e\u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/mokeeqian\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/45727636?v=4?s=100\" width=\"100px;\" alt=\"\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eAC手动机\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/ZhiningLiu1998/self-paced-ensemble/issues?q=author%3Amokeeqian\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e\u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\u003ca href=\"https://www.linkedin.com/in/carlo-moro-4a20a7132\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/21183273?v=4?s=100\" width=\"100px;\" alt=\"\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eCarlo Moro\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#ideas-cnmoro\" title=\"Ideas, Planning, \u0026 Feedback\"\u003e🤔\u003c/a\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\n\u003c!-- markdownlint-restore --\u003e\n\u003c!-- prettier-ignore-end --\u003e\n\n\u003c!-- ALL-CONTRIBUTORS-LIST:END --\u003e\n\nThis project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzhiningliu1998%2Fself-paced-ensemble","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzhiningliu1998%2Fself-paced-ensemble","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzhiningliu1998%2Fself-paced-ensemble/lists"}