[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119449-en":3,"doc-seo-119449-105":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":4,"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},119449,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Credit Card Fraud Detection through Machine Learning - A Comparative Study","This thesis presents a data-driven approach to credit card fraud detection using machine learning and focuses on comparative evaluation across multiple modeling and hyperparameter optimization strategies. It begins with data understanding and exploration, covering feature types, table preparation, merging procedures, and class imbalance characteristics. Subsequent stages perform data cleaning, then apply hyperparameter optimization algorithms including TPE and CMA-ES, using evaluation metrics to guide selection. Final experiments tune models such as XGBoost and LightGBM and analyze comparative performance under defined experimental settings.","CALIFORNIA STATE UNIVERSITY, NORTHRIDGE  \nCREDIT CARD FRAUD DETECTION THROUGH MACHINE LEARNING: A COMPARATIVE STUDY  \nA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science  \nby  \nYi Xia  \nCopyright by Yi Xia 2023  \nThe thesis of Yi Xia is approved:  \n\n| Taehyung Wang |\n| --- |\n| Felix Robinovich |\n\nLi Liu, Chair  \nDate  \nDate  \nDate  \nCalifornia State University, Northridge  \nAcknowledgments  \nI would like to express my sincere gratitude to Professor Li Liu, Taehyung Wang, and Felix Robinovich for their invaluable academic help and guidance throughout my thesis journey. Their expertise and support have been instrumental in my success, and I am truly grateful for everything they have done.  \nI would also like to extend my heartfelt appreciation to my husband Kunlong for his unwavering encouragement and trust during times of difficulty and uncertainty. Additionally, I would like to thank my friends Hexuan, Hao, Hui, Liang and Zhen for their invaluable support and assistance throughout all aspects of my 2-year graduate study life.  \nFinally, I would like to thank every professor, group member, and classmate I had the pleasure of meeting during my 2 years of study. It was an honor to have had the opportunity to meet everyone at CSUN and this experience will undoubtedly be one of the most significant in my life.  \nTable of Contents  \nCopyright .......................................................................................................................................ii  \nSignature page...............................................................................................................................iii  \nAcknowledgments......................................................................................................................... iv  \nList of Tables ...............................................................................................................................vii  \nList of Figures .............................................................................................................................viii  \nAbstract ......................................................................................................................................... ix  \nChapter 1 Introduction ................................................................................................................. 1  \n1.1 Objective .........................................................................................................................2  \n1.2 Problem Statement ..........................................................................................................2  \n1.3 Related Work................................................................................................................... 3  \n1.4 Outline .............................................................................................................................4  \nChapter 2 Data Understanding and Exploration .......................................................................... 6  \n2.1 Shape of Tables ............................................................................................................... 6  \n2.2 Type of Features .............................................................................................................. 7  \n2.3 Merging Tables ............................................................................................................... 8  \n2.4 Imbalanced data............................................................................................................. 10  \nChapter 3 Data Cleaning ............................................................................................................ 11  \nChapter 4 Hyperparameters Optimization Algorithms .............................................................. 13  \n4.1 Tree-structured Parzen Estimator Approach (TPE) ....................................","cbCairjhngydNMT8","https://ap.wps.com/l/cbCairjhngydNMT8","pdf",1127141,1,45,"English","en",105,"# Acknowledgments\n# Table of Contents\n## Chapter 1 Introduction\n## Chapter 2 Data Understanding and Exploration\n## Chapter 3 Data Cleaning\n## Chapter 4 Hyperparameters Optimization Algorithms\n## Chapter 5 Hyperparameter Tuning\n## Chapter 6 Comparison","[{\"question\":\"What is the primary goal of the thesis?\",\"answer\":\"The thesis aims to detect credit card fraud using machine learning and to compare different approaches for model building and hyperparameter optimization.\"},{\"question\":\"How does the thesis handle data characteristics before modeling?\",\"answer\":\"It includes data understanding and exploration, data cleaning, and considers issues such as feature types, merged tables, and imbalanced data.\"},{\"question\":\"Which optimization and models are compared in the experiments?\",\"answer\":\"Hyperparameter optimization strategies include TPE and CMA-ES, and the tuning experiments cover models such as XGBoost and LightGBM.\"}]","Credit Card Fraud Detection through Machine Learning - 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