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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.12154 |
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| _version_ | 1866915066764328960 |
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| author | Chen, Sihan Qian, Zhuangzhuang Siu, Wingchun Hu, Xingcan Li, Jiaqi Li, Shawn Qin, Yuehan Yang, Tiankai Xiao, Zhuo Ye, Wanghao Zhang, Yichi Dong, Yushun Zhao, Yue |
| author_facet | Chen, Sihan Qian, Zhuangzhuang Siu, Wingchun Hu, Xingcan Li, Jiaqi Li, Shawn Qin, Yuehan Yang, Tiankai Xiao, Zhuo Ye, Wanghao Zhang, Yichi Dong, Yushun Zhao, Yue |
| contents | Outlier detection (OD), also known as anomaly detection, is a critical machine learning (ML) task with applications in fraud detection, network intrusion detection, clickstream analysis, recommendation systems, and social network moderation. Among open-source libraries for outlier detection, the Python Outlier Detection (PyOD) library is the most widely adopted, with over 8,500 GitHub stars, 25 million downloads, and diverse industry usage. However, PyOD currently faces three limitations: (1) insufficient coverage of modern deep learning algorithms, (2) fragmented implementations across PyTorch and TensorFlow, and (3) no automated model selection, making it hard for non-experts.
To address these issues, we present PyOD Version 2 (PyOD 2), which integrates 12 state-of-the-art deep learning models into a unified PyTorch framework and introduces a large language model (LLM)-based pipeline for automated OD model selection. These improvements simplify OD workflows, provide access to 45 algorithms, and deliver robust performance on various datasets. In this paper, we demonstrate how PyOD 2 streamlines the deployment and automation of OD models and sets a new standard in both research and industry. PyOD 2 is accessible at [https://github.com/yzhao062/pyod](https://github.com/yzhao062/pyod). This study aligns with the Web Mining and Content Analysis track, addressing topics such as the robustness of Web mining methods and the quality of algorithmically-generated Web data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_12154 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection Chen, Sihan Qian, Zhuangzhuang Siu, Wingchun Hu, Xingcan Li, Jiaqi Li, Shawn Qin, Yuehan Yang, Tiankai Xiao, Zhuo Ye, Wanghao Zhang, Yichi Dong, Yushun Zhao, Yue Machine Learning Artificial Intelligence Computation and Language Outlier detection (OD), also known as anomaly detection, is a critical machine learning (ML) task with applications in fraud detection, network intrusion detection, clickstream analysis, recommendation systems, and social network moderation. Among open-source libraries for outlier detection, the Python Outlier Detection (PyOD) library is the most widely adopted, with over 8,500 GitHub stars, 25 million downloads, and diverse industry usage. However, PyOD currently faces three limitations: (1) insufficient coverage of modern deep learning algorithms, (2) fragmented implementations across PyTorch and TensorFlow, and (3) no automated model selection, making it hard for non-experts. To address these issues, we present PyOD Version 2 (PyOD 2), which integrates 12 state-of-the-art deep learning models into a unified PyTorch framework and introduces a large language model (LLM)-based pipeline for automated OD model selection. These improvements simplify OD workflows, provide access to 45 algorithms, and deliver robust performance on various datasets. In this paper, we demonstrate how PyOD 2 streamlines the deployment and automation of OD models and sets a new standard in both research and industry. PyOD 2 is accessible at [https://github.com/yzhao062/pyod](https://github.com/yzhao062/pyod). This study aligns with the Web Mining and Content Analysis track, addressing topics such as the robustness of Web mining methods and the quality of algorithmically-generated Web data. |
| title | PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2412.12154 |