[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125287-en":3,"doc-seo-125287-105":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},125287,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Feature selection and hierarchical modelling in tree-based machine learning models - dissertation","Feature selection and hierarchical modelling are addressed within tree-based machine learning, focusing on improving interpretability and predictive performance while controlling complexity. The dissertation develops generalizable gain penalization for feature selection in tree ensembles, including guidance for choosing the gain function and depth-related parameters, and validates methods through simulations and real-data classification. A hierarchical extension of Bayesian additive regression trees is also proposed, introducing hierarchical embedded structure with prior specification and parameter updating, and evaluating applications and results.","Feature selection and hierarchical modelling in tree-based machine learning models  \nA dissertation submitted for the degree of  \nDoctor of Philosophy  \nBy:  \nBruna Davies Wundervald  \nUnder the supervision of:  \nProf. Andrew C. Parnell  \nDr. Katarina Domijan  \nHamilton Institute  \nNational University of Ireland Maynooth Ollscoil na hÉireann, Má Nuad  \nMarch 2023  \nTo my parents and sister, who have given me everything I needed to be the person I am today,  \nTo my Ph.D. supervisors, Andrew Parnell and Katarina Domijan, for the unlimited patience and support, who have made my Ph.D. experience incredibly smooth and enjoyable,  \nTo Walmes Zeviani, Wagner Bonat, and Paulo Justiniano, my undergrad teachers, supervisors, and friends, who gave me the tools and courage to do a Ph.D. ,  \nTo Julio Trecenti, Daniel Falbel, Fernando Correa, Athos Damiani, William Amorim and Caio Lente, who are as good statisticians and programmers as they are friends, and who have made an unmeasurable difference in my academic career,  \nTo João Pedro Rimenzoski, Bruno Ritter and Raissa Mariana, who are the best people I ever met, and whom I will love no matter what,  \nTo all my other friends, who have brought joy and love to my life an infinite number of times. I most certainly wouldn’t be where I am today if it wasn’t for them,  \n\"E tudo que passou, valeu  \nPra ver que é real  \nParaíso total  \nÉ tudo quente e colorido\"  \nDeclaration  \nI hereby declare that I have produced this manuscript without the prohibited assistance of any third parties and without making use of aids other than those specified.  \nThe thesis work was conducted from September 2018 to March 2023 under the supervision of Professor Andrew C. Parnell and Dr. Katarina Domijan in the Hamilton Institute, National University of Ireland Maynooth.  \nBruna Davies Wundervald, Maynooth, Ireland,  \nMarch 2023 .  \nSponsor  \nThis work was supported by a Science Foundation Ireland Career Development Award grant number 17/CDA/4695 .  \nCollaborations  \nAndrew C. Parnell: As my supervisor, Professor Parnell (Maynooth University) supervised and collaborated on the work of all chapters.  \nKatarina Domijan: As my supervisor, Dr. Domijan (Maynooth University) supervised the work of all chapters.  \nPublications  \nThe chapters presented in this thesis have been either published or submitted to peer-reviewed journals. Chapter 3 has already been published in the journal IEEE Access and Chapter 4 is currently under review of the Stat journal (the reviews have already arrived) . Chapter 5 is soon to be submitted to a statistics and machine learning journal. In all papers, Bruna Wundervald is the main author of the paper.  \nPeer-reviewed journal article:  \n• B. Wundervald, A. C. Parnell and K. Domijan, (2020) \"Generalizing Gain Penalization for Feature Selection in Tree-Based Models\", in IEEE Access, pp. 190231-190239, 2020, doi: 10.1109/ACCESS.2020.3032095 .  \nSubmitted articles (under review):  \n• B. Wundervald, A. C. Parnell and K. Domijan,(2022) . Hierarchical Embedded Bayesian Additive Regression Trees. Under review in the journal Stats. arXiv preprint: [https://arxiv. org/abs/2204.07207](https://arxiv. org/abs/2204.07207) .  \nTo Be Submitted:  \n• Prado, B. Wundervald, A. C. Parnell and K. Domijan, (2023) . Hierarchical Embedded Bayesian Additive Regression Trees for Crossed and Nested Random Effects.  \nContents  \nAbstract x  \nList of Figures xii  \nList of Tables xvi  \n1 Introduction 1  \n1. 1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1  \n1.2 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 4  \n2 Tree-based algorithms 7  \n2.1 Tree-based algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 7  \n2.2 Random Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10  \n2.3 Bayesian Additive Regression Trees .................. 14  \n2.3.1 The BART fitting algorithm .................. 19  \n2.4 Conclusions . . . . . . . . . . . . . . . . . . . . ","cbCaingJ0Sw9xnXk","https://ap.wps.com/l/cbCaingJ0Sw9xnXk","pdf",4844347,1,122,"English","en",105,"# Contents\n## Abstract\n## List of Figures\n## List of Tables\n## 1 Introduction\n## 2 Tree-based algorithms\n## 3 Generalizing Gain Penalization for Feature Selection in Tree-based Models\n## 4 Hierarchical Embedded Bayesian Additive Regression Trees","[{\"question\":\"What is the central problem addressed in the dissertation?\",\"answer\":\"The dissertation investigates feature selection and hierarchical modelling within tree-based machine learning models, aiming to improve performance while managing model complexity.\"},{\"question\":\"How does the dissertation approach feature selection in tree-based models?\",\"answer\":\"It proposes generalizing gain penalization for feature selection in tree ensembles, including choices for the gain function and a depth parameter.\"},{\"question\":\"What is the role of hierarchical modelling in the proposed BART approach?\",\"answer\":\"It introduces Hierarchical Embedded Bayesian Additive Regression Trees (HEBART) by specifying hierarchical embedded priors and detailing how parameters are updated, then evaluating applications and results.\"}]","Feature selection and hierarchical modelling in tree-based machine learning models - 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