[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118191-en":3,"doc-seo-118191-105":29,"detail-sidebar-cat-0-en-105":90},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118191,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A Systems Theoretic Approach to Online Machine Learning","The machine learning formulation of online learning lacks a formal systems theoretic view that connects learning algorithms to system structure, dynamics, and behavior. This work builds a top-down framework based on input-output systems, providing a novel definition of online learning and identifying key design parameters. It treats concept drift as a formal system behavior characteristic rather than an ad hoc phenomenon. A healthcare provider fraud detection case study grounds the discussion in a real-world online learning challenge.","A Systems Theoretic Approach to Online Machine  \nLearning  \nAnli du Preez, Peter Beling, Tyler Cody􀀃  \nGrado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA  \nResponsible General Intelligence Lab, Virginia Tech, Arlington, VA, USA  \n􀀃 Corresponding Author: tcody@vt.edu  \narXiv :2404 .03775v 1 [ cs .LG] 4 Apr 2024  \nAbstract—The machine learning formulation of online learning is incomplete from a systems theoretic perspective. Typically, machine learning research emphasizes domains and tasks, and a problem solving worldview. It focuses on algorithm parameters, features, and samples, and neglects the perspective offered by considering system structure and system behavior or dynamics. Online learning is an active 􀀂eld of research and has been widely explored in terms of statistical theory and computational algorithms, however, in general, the literature still lacks formal system theoretical frameworks for modeling online learning systems and resolving systems-related concept drift issues. Furthermore, while the machine learning formulation serves to classify methods and literature, the systems theoretic formulation presented herein serves to provide a framework for the top-down design of online learning systems, including a novel de􀀂nition of online learning and the identi􀀂cation of key design parameters. The framework is formulated in terms of input-output systems and is further divided into system structure and system behavior. Concept drift is a critical challenge faced in online learning, and this work formally approaches it as part of the system behavior characteristics. Healthcare provider fraud detection using machine learning is used as a case study throughout the paper to ground the discussion in a real-world online learning challenge.  \nIndex Terms—machine learning, online learning, systems theory, learning theory  \nI. INTRODUCTION  \nUnlike typical machine learning (ML) methods that assume training examples are available before the learning task, online learning (OL) addresses the challenge of most real-world problems where training data arrives sequentially over time [1] . OL is a method of ML where the goal of the learner is to sequentially update itself when presented with a sequential stream of data in order to remain the best predictor for future data at every time step [2] . Thus, the learner seeks to accumulate knowledge and update it according to the changes in the data distribution over time to maintain reliable prediction performance. OL is relevant to all types of learning and is most widely studied in the context of supervised learning, where full feedback information (i.e., labeled data) is assumed to always be available to learn from.  \nIn this paper, a systems theoretic framework is developed to aid in the systems understanding of OL algorithms and the associated knowledge updating process in terms of systems terminology. The presented systems theoretic approach allows for the top-down design of OL systems, because it considers OL systems in terms of their abstract, general-systems nature,  \nas opposed to the speci􀀂c details of OL solution methods. Furthermore, a novel de􀀂nition of OL as well as the identi􀀂 -cation of key algorithm design parameters in OL systems are provided.  \nThroughout the paper, healthcare provider fraud detection via ML will be used as a running example to ground the theoretical discussion. In healthcare fraud, the need for OLarises in numerous scenarios. Fraudsters adjust their behavior over time to remain successful and undetected, hence the data distributions change over time and the applied learner should update accordingly to remain relevant and trustworthy. Healthcare providers can change a variety of factors, such as their billing prices and procedures, prescription behavior, and treatments administered, to disguise their fraud. It is expected that the fraud detection learning system will remember previous fraudulent behavior to identify t","cbCaih9kQQ3VKMn0","https://ap.wps.com/l/cbCaih9kQQ3VKMn0","pdf",167970,1,"English","en",105,"# Introduction\n## Online Learning Background\n## Systems Theoretic Framework\n# Background\n## Online Learning Background\n# Framework Overview\n## System Structure\n## System Behavior\n# Case Study\n## Healthcare Provider Fraud Detection\n# Conclusion\n## Limitations and Future Research","[{\"question\":\"What gap does the paper address in existing online learning research?\",\"answer\":\"It argues that common machine learning formulations lack formal systems theoretic frameworks for modeling online learning systems and handling systems-related concept drift.\"},{\"question\":\"How does the proposed framework describe online learning systems?\",\"answer\":\"It formulates online learning as input-output systems and separates the discussion into system structure and system behavior to support top-down design.\"},{\"question\":\"Why is concept drift treated as part of system behavior?\",\"answer\":\"The paper approaches concept drift as a critical challenge formally within the system behavior characteristics of online learning, rather than treating it only statistically or algorithmically.\"}]","A Systems Theoretic Approach to Online Machine Learning | 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gap does the paper address in existing online learning research?","Question",{"text":74,"@type":75},"It argues that common machine learning formulations lack formal systems theoretic frameworks for modeling online learning systems and handling systems-related concept drift.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the proposed framework describe online learning systems?",{"text":79,"@type":75},"It formulates online learning as input-output systems and separates the discussion into system structure and system behavior to support top-down design.",{"name":81,"@type":72,"acceptedAnswer":82},"Why is concept drift treated as part of system behavior?",{"text":83,"@type":75},"The paper approaches concept drift as a critical challenge formally within the system behavior characteristics of online learning, rather than treating it only statistically or 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