[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119926-en":3,"doc-seo-119926-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119926,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Anomaly Detection with Automated and Active Machine Learning - Thesis","Anomaly detection identifies unusual behaviors across data and is widely used in practical systems. Traditional machine learning approaches still require substantial engineering to build effective pipelines, and unsupervised training on imbalanced data often produces high false positive rates that limit adoption. This thesis proposes a unified anomaly detection module for automated detection, and an active anomaly detection method to suppress false positives. The work addresses I/O interface design, detection algorithm interface design, and query sampling for active learning.","ANOMALY DETECTION WITH AUTOMATED AND ACTIVE MACHINE  \nLEARNING  \nA Thesis  \nby  \nMINGYANG WAN  \nSubmitted to the Graduate and Professional School of Texas A&M University  \nin partial fulfillment of the requirements for the degree of MASTER OF SCIENCE  \nChair of Committee, Xia \"Ben\" Hu Committee Members, Theodora Chaspari  \nNa Zou  \nHead of Department, Scott Schaefer  \nDecember 2022  \nMajor Subject: Computer Science  \nCopyright 2022 Mingyang Wan  \nABSTRACT  \nAnomaly detection, which aims to identify unusual or uncommon behaviors in data, has many real-world applications. While numerous machine learning algorithms have been developed for anomaly detection, we often still need extensive engineering efforts to develop effective machine learning pipelines. Moreover, anomaly detectors can have high false positive rates because they are often unsupervised and trained on imbalanced data, which significantly impedes the practical use of anomaly detection algorithms. To bridge the gaps, in this thesis, we aim to provide a unified anomaly detection module to support automated anomaly detection in various real-world applications and an active anomaly detection algorithm to reduce the high false positive rates. It is challenging to achieve these two objectives because of several unanswered research questions in I/O interface design, detection algorithm interface design, and the difficulty of selecting the right sample for query in active anomaly detection. Through answering these questions, we devise a consistent I/O interface for different types of anomalies, a unified objected-oriented design of anomaly detection algorithms, and a novel active anomaly detection algorithm based on meta-learning. Extensive experiments show that the implemented anomaly detection module is effective and that the proposed active anomaly detection algorithm outperforms the existing baselines. Part of the research outcomes has been integrated into the TODS package, a time-series anomaly detection system.  \nACKNOWLEDGMENTS  \nThis thesis has received the support and assistance of many individuals. I would like to express my sincere gratitude to all of them.  \nI would like to thank my committee members, Dr. Xia \"Ben\" Hu, Dr. Na Zou and Dr. Theodora Chaspari for their guidance and help.  \nAlso, I would like to thank the group members in the DATA (Data Analyticsat Texas A&M) Lab of the Department of Computer Science and Engineering for their advice and collaboration with my research.  \nFinally, I would like to thank Texas A&M University for the resources and support during my study.  \nCONTRIBUTORS AND FUNDING SOURCES  \nContributors  \nThis work was supported by the thesis committee consisting of Professor Xia\"Ben\" Hu (advisor) and Professor Theodora Chaspari from the Department of Computer Science and Engineering, and Professor Na Zou from the Department of Engineering Technology and Industrial Distribution.  \nThe conceptualization, design, implementation, and experimentation in this work is a collaborated work between Kwei-Herng Lai, Daochen Zha, Guanchu Wang, Junjie Xu, Yue Zhao, Devesh Kumar, Yile Chen, Purav Zumkhawaka, Diego Martinez.  \nFunding Sources  \nThe research work was supported under the Research Assistant title from Texas A&M University.  \nTABLE OF CONTENTS  \nPage  \nABSTRACT .................................. ii  \nACKNOWLEDGMENTS ........................... iii  \nCONTRIBUTORS AND FUNDING SOURCES .............. iv  \nTABLE OF CONTENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . v  \nLIST OF FIGURES .............................. vii  \nLIST OF TABLES .............................. x  \n1. INTRODUCTION ............................ 1  \n1.1 Background and Motivation .................... 1  \n1.2 Thesis Contributions ........................ 3  \n2. RELATED WORK ............................ 6  \n2.1 Time-Series Anomaly Detection in Machine Learning ....... 6  \n2.1.1 Statistical-based Algorithms ................ 6  \n2.1.2 Distance-based Algorithms ........","cbCaiaW9EgBbkwTh","https://ap.wps.com/l/cbCaiaW9EgBbkwTh","pdf",2575493,1,56,"English","en",105,"# Abstract\n# Acknowledgments\n# Contributors and Funding Sources\n# Table of Contents\n# List of Figures\n# List of Tables\n# 1. Introduction\n## 1.1 Background and Motivation\n## 1.2 Thesis Contributions\n# 2. Related Work\n## 2.1 Time-Series Anomaly Detection in Machine Learning\n## 2.2 Leveraging Labels for Anomaly Detection\n## 2.3 Reinforcement Learning\n# 3. Problem Statement\n## 3.1 Designing Consistent I/O for Anomaly Detection Module\n## 3.2 Enabling Unified Interfaces for Anomaly Detection Algorithms\n## 3.3 Reducing False Positive Rates with Active Learning\n# 4. The Proposed Solution\n## 4.1 Designing a Consistent I/O Interface\n## 4.2 Implementing Unified Interfaces for Anomaly Detection Algorithms\n## 4.3 Reducing False Positive Rates with Active Learning\n# 5. Experiments\n## 5.1 Evaluation of the Anomaly Detection Module in Pipeline\n## 5.2 Evaluation of Meta-AAD\n## 5.3 Experimental Results\n# 6. Conclusion\n# References\n# Appendix A. 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