[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119397-en":3,"doc-seo-119397-105":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},119397,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","EXPLORING THE IMPACT OF FEATURE SELECTION TECHNIQUES ON MACHINE LEARNING PERFORMANCE","Feature selection is a pivotal machine learning stage aimed at identifying the most relevant, informative characteristics to enhance model quality. This thesis examines the consequences of multiple feature selection techniques on machine learning performance and evaluates their effectiveness across different domains. It defines feature selection and explains why it improves models, surveys filter, wrapper, and embedded methods, and analyzes evaluation criteria. Traditional methods including Chi-Square, Information Gain, correlation-based approaches, and Mutual Information are compared, addressing dimensionality reduction, domain-specific performance, and practical challenges such as redundancy and feature interaction, and considering supervised, unsupervised, hybrid, and deep learning contexts.","REPUBLIC OF TÜRKİYE ALTINBAŞ UNIVERSITY Institute of Graduate Studies Information Technologies  \nEXPLORING THE IMPACT OF FEATURESELECTION TECHNIQUES ON MACHINE LEARNING PERFORMANCE  \nOmar Ali ALMAJMAEE  \nMaster’s Thesis  \nSupervisor  \nAsst. Prof. Dr. Ayça Kurnaz TÜRKBEN  \nIstanbul, 2023  \nEXPLORING THE IMPACT OF FEATURE SELECTION TECHNIQUES ON MACHINE LEARNING PERFORMANCE  \nOmar Ali ALMAJMAEE  \nInformation Technologies  \nMaster’s Thesis  \nALTINBAŞ UNIVERSITY  \nThe thesis titled EXPLORING THE IMPACT OF FEATURE SELECTION TECHNIQUES ON MACHINE LEARNING PERFORMANCE prepared by OMAR ALI ALMAJMAEE and submitted on 07/08/2023 has been accepted unanimously for the degree of Master of Science in Information Technology.  \nAsst. Prof. Dr. Ayça Kurnaz TÜRKBEN  \nSupervisor  \nThesis Defense Committee Members:  \nAsst. Prof. Dr. Ayça Kurnaz TÜRKBEN  \nAsst. Prof. Dr. Abdullahi Abdu  \nIBRAHIM  \nAsst. Prof. Dr. Serdar KARGIN  \nDepartment of Software Engineering,  \nAltınbaş University  \nDepartment of Computer Engineering,  \nAltınbaş University  \nDepartment of Biomedical Engineering,  \nArel University  \n__________________  \n__________________  \n__________________  \nI hereby declare that this thesis meets all format and submission requirements for a Master’s thesis.  \nSubmission date of the thesis to Institute of Graduate Studies:  / /   \nI hereby declare that all information/data presented in this graduation project has been obtained in full accordance with academic rules and ethical conduct. I also declare all unoriginal materials and conclusions have been cited in the text and all references mentioned in the Reference List have been cited in the text, and viceversa as required by the abovementioned rules and conduct.  \nOmar Ali ALMAJMAEE  \nSignature  \nABSTRACT  \nEXPLORING THE IMPACT OF FEATURE SELECTION TECHNIQUES ON MACHINE LEARNING PERFORMANCE  \nALMAJMAEE, Omar Ali  \nM.Sc., Information Technologies, Altınbaş University,  \nSupervisor: Asst. Prof. Dr. Ayça Kurnaz TÜRKBEN  \nDate: 08/2023  \nPages: 72  \nFeature selection is a crucial stage in machine learning, with the overarching goal of identifying the most relevant and informative characteristics with which to improve the model's overall performance. This thesis explores repercussions of various feature selection techniques on machine learning performance and investigates their effectiveness across different domains. The initial sections provide a comprehensive understanding of featureselection, including its definition and significance in enhancing machine learning models. Different types of feature selection methods are discussed, encompassing filter, wrapper, and embedded approaches. Evaluation criteria for assessing the effectiveness of feature selection techniques are also presented. Traditional feature selection techniques, such as Chi-Square, Information Gain, Correlation Coefficient, and Mutual Information, are thoroughly examined, considering their strengths and limitations. The thesis evaluates the impact of these techniques on machine learning performance across various domains, emphasizing domain-specific performance, dimensionality reduction, performance improvement, and the associated challenges and considerations. Supervised, unsupervised, and hybrid featureselection techniques are explored, providing insights into their methodologies and applicability. Additionally, the thesis investigates feature selection in the context of deep learning, highlighting its unique considerations and implications. Challenges and considerations in feature selection are addressed, including issues related to highdimensional data, feature interaction, and redundancy. The thesis surveys application domains where feature selection has been successfully applied, demonstrating its effectiveness across diverse fields. The thesis concludes with future directions and research  \ngaps in feature selection, highlighting areas that require further investigation and development. Overall, this thesis contributes t","cbCaiavUrljlt63y","https://ap.wps.com/l/cbCaiavUrljlt63y","pdf",1561317,1,76,"English","en",105,"# 1. INTRODUCTION\n## 1.1 INTRODUCTION\n## 1.2 FUNDAMENTALS OF FEATURE SELECTION\n## 1.2.1 Definition of Feature Selection and its Significance\n## 1.2.2 Types of Feature Selection Methods\n## 1.3 TRADITIONAL FEATURE SELECTION TECHNIQUES\n## 1.3.1 Chi-Square\n## 1.3.2 Information Gain\n## 1.3.3 Correlation Coefficient\n## 1.4 EVALUATION OF THEIR IMPACT ON MACHINE LEARNING PERFORMANCE ACROSS VARIOUS DOMAINS","[{\"question\":\"What is the main goal of feature selection in this thesis?\",\"answer\":\"To identify the most relevant and informative characteristics that improve overall machine learning model performance.\"},{\"question\":\"Which types of feature selection methods are covered?\",\"answer\":\"The thesis discusses filter, wrapper, and embedded approaches, and also explores supervised, unsupervised, and hybrid techniques.\"},{\"question\":\"How are traditional feature selection techniques assessed?\",\"answer\":\"Methods such as Chi-Square, Information Gain, correlation coefficient, and Mutual Information are examined for their strengths, limitations, and impact on performance across different domains.\"}]","EXPLORING THE IMPACT OF FEATURE SELECTION TECHNIQUES ON MACHINE LEARNING PERFORMANCE | 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