[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122732-en":3,"doc-seo-122732-105":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},122732,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","GENETIC PROGRAMMING TO OPTIMIZE PERFORMANCE OF MACHINE LEARNING ALGORITHMS ON UNBALANCED DATA SET","Real-world datasets are frequently imbalanced, with class distributions that are biased or skewed, which typically reduces predictive performance for machine learning classifiers built for balanced or near-balanced class assumptions. This project applies preprocessing strategies to correct imbalance using SMOTE for oversampling and random undersampling for undersampling. After balancing, genetic programming performs feature selection to improve performance and efficiency. Experiments use an imbalanced UCI bank marketing dataset and compare Decision Tree, Logistic Regression, KNN, and SVM with multiple evaluation metrics.","California State University, San Bernardino  \nCSUSB ScholarWorks  \n\n| Electronic Theses, Projects, and Dissertations | Office of Graduate Studies |\n| --- | --- |\n| 8-2023\u003Cbr>GENETIC PROGRAMMING TO OPTIMIZE PERFORMANCE OF MACHINE LEARNING ALGORITHMS ON UNBALANCED DATA SET Asitha Thumpati\u003Cbr>Follow this and additional works at: [https://scholarworks.lib.csusb.edu/etd](https://scholarworks.lib.csusb.edu/etd)\u003Cbr> Part of the Data Science Commons |  |\n\nRecommended Citation  \nThumpati, Asitha, \"GENETIC PROGRAMMING TO OPTIMIZE PERFORMANCE OF MACHINE LEARNING ALGORITHMS ON UNBALANCED DATA SET\" (2023) . Electronic Theses, Projects, and Dissertations. 1777.  \n[https://scholarworks.lib.csusb.edu/etd/1777](https://scholarworks.lib.csusb.edu/etd/1777)  \nThis Project is brought to you for free and open access by the Office of Graduate Studies at CSUSB ScholarWorks. It has been accepted for inclusion in Electronic Theses, Projects, and Dissertations by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [scholarworks@csusb.edu](scholarworks@csusb.edu).  \nGENETIC PROGRAMMING TO OPTIMIZE PERFORMANCE OF  \nMACHINE LEARNING ALGORITHMS ON UNBALANCED DATA SET  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nIn Partial Fulfillment of the Requirements for the Degree Master of Science in Computer Science  \nby Asitha Thumpati  \nAugust 2023  \nGENETIC PROGRAMMING TO OPTIMIZE PERFORMANCE OF  \nMACHINE LEARNING ALGORITHMS ON UNBALANCED DATA SET  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nby  \nAsitha Thumpati  \nAugust 2023  \nApproved by:  \nDr. Yan Zhang, Committee Advisor, Computer Science and Engineering  \nDr. Jennifer Jin, Committee Member  \nDr. Amir Ghasemkhani, Committee Member  \n© 2023 Asitha Thumpati  \nABSTRACT  \nData collected from the real world is often imbalanced, meaning that the distribution of data across known classes is biased or skewed. When using machine learning classification models on such imbalanced data, predictive performance tends to be lower because these models are designed with the assumption of balanced classes or a relatively equal number of instances for each class. To address this issue, we employ data preprocessing techniques such as SMOTE (Synthetic Minority Oversampling Technique) for oversampling data and random undersampling for undersampling data on unbalanced datasets. Once the dataset is balanced, genetic programming is utilized for feature selection to enhance performance and efficiency.  \nFor this experiment, we consider an imbalanced bank marketing dataset from the UCI Machine Learning Repository. To assess the effectiveness of the technique, it is implemented on four different classification algorithms: Decision Tree, Logistic Regression, KNN (K-Nearest Neighbors), and SVM (Support Vector Machines) . Various metrics including accuracy, balanced accuracy, recall, F-score, ROC (Receiver Operating Characteristics) curve, and PR (PrecisionRecall) curve are compared for unbalanced data, oversampled data, undersampled data, and cleaned data with Tomek-Links for each algorithm.  \nThe results indicate that all four algorithms perform better when oversampling the minority class to half of the majority class and undersampling the majority class examples to match the minority class, followed by performing Tomek-Links on the balanced dataset.  \nACKNOWLEDGEMENTS  \nI wish to express my deep sense of gratitude to my project advisor Dr. Yan Zhang, Computer Science of Engineering for her able guidance and useful suggestions, which helped me in completing the project work, in time. She has been a great source of inspiration. I would like to thank my project committee members Dr. Jennifer Jin and Dr. Amir Ghasemkhani for their continuous support. I also thank all my faculty and university who have helped me through this entire process of completing my degree.  \nFinally , yet importantly , I would like to express my he","cbCaipqe9RqcUS4v","https://ap.wps.com/l/cbCaipqe9RqcUS4v","pdf",1100250,1,55,"English","en",105,"# ABSTRACT\n# ACKNOWLEDGEMENTS\n# LIST OF TABLES\n# LIST OF FIGURES\n# CHAPTER ONE: INTRODUCTION\n## 1.1 Problem Statement\n## 1.2 Organization of Project\n# CHAPTER TWO: LITERATURE REVIEW\n# CHAPTER THREE: PROPOSED METHOD\n## 3.1 Data Source\n## 3.2 Data Preprocessing and Feature Selection\n## 3.3 Methodology\n### 3.3.1 Decision Tree\n### 3.3.2 Logistic Regression\n### 3.3.3 K-Nearest Neibhours\n### 3.3.4 Suport Vector Machines\n# CHAPTER FOUR: SYSTEM DESIGN\n## 4.1 System Architeture","[{\"question\":\"Why does model performance drop on imbalanced datasets?\",\"answer\":\"Because many classifiers assume balanced classes or similar numbers of instances per class, imbalance biases predictions and lowers predictive performance.\"},{\"question\":\"What preprocessing methods are used to handle imbalance?\",\"answer\":\"The study uses SMOTE to oversample the minority class and random undersampling to reduce the majority class, producing a more balanced dataset.\"},{\"question\":\"How is genetic programming applied in the workflow?\",\"answer\":\"After balancing the dataset, genetic programming is used for feature selection to improve both performance and efficiency of the learning pipeline.\"}]","GENETIC PROGRAMMING TO OPTIMIZE PERFORMANCE OF MACHINE LEARNING ALGORITHMS ON UNBALANCED DATA SET | 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