[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118791-en":3,"doc-seo-118791-105":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},118791,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Estimation of Missing Data in Ecological Studies Using Machine Learning Techniques","This thesis investigates methods for handling missing or challenging information in ecological studies, with emphasis on count data that is often zero-inflated and overdispersed. It addresses a key population-analysis risk: ignoring overdispersion can produce biased parameter estimates and lead to spurious identification of predictors as biologically meaningful. Machine learning techniques are developed and evaluated to improve estimation under these data issues. The work is structured around three articles targeting core objectives and comparing model behavior for ecological prediction tasks.","Estimation of Missing Data in Ecological Studies Using Machine Learning Techniques  \nB Sidumo  \n [orcid.org/0000-000](orcid.org/0000-0002-4267-9651)[2-4267-9651](orcid.org/0000-0002-4267-9651)  \nThesis submitted in fulfillment of the requirements for the degree Doctor of Philosophy in Operational Research at the North-West  \nUniversity  \nPromoter: Dr E Sonono  \nCo-promoter: Dr I Takaidza  \nExamination:November 2022  \nStudent number: 31494498  \nDeclaration  \nBy submitting this thesis, I declare that the entire work contains no material that have been accepted for any other degree or diploma in any university and to the best of my knowledge and belief, it contains no material previously published or written by another person, except where due reference has been made in the text. All sources of help have been acknowledged.  \n----------------  \nBonelwa Sidumo  \n16 November 2022  \n----------------------  \nDate  \nCopyright ➞ 2022 North-West University All rights reserved.  \ni  \nDedication  \nI dedicate this thesis to my late father Mxolisi Sidumo, as it was his wish to see me one day wearing ibhatyi ebomvu (a red gown) .  \nii  \nAcknowledgements  \nFirst and foremost, I would like to give gratitude to the Almighty God for providing me with the opportunity and the determination to carry out this study at North-West University. My special thanks goes to my supervisor, Dr Energy Sonono who has patiently guided me and my co-supervisor Dr Isaac Takaidza. Without my supervisors’guidance and help, this thesis would not have been possible.  \nI would also like to thank Laban Musinguzi and other research team members of the National Fisheries Resources Research Institute of Uganda for the kind permission to use their data. A special thanks to my sister Namhla Mkiva, words cannot express how grateful I am for your emotional support throughout this experience. I would like to thank the North-West University nGAP manager Prof Susan Visser for her endless support throughout my PhD studies. I am indebted to the School of Mathematical and Statistical Sciences and colleagues for providing me different support.  \nFinally, I would like to thank my family for their support throughout this journey. I could not accomplish so much without their support. Words cannot express how grateful I am to my mother Nophuthumile Sidumo, for all of the sacrifices she has made on my behalf. Her prayers for me were what sustained me thus far. To my beloved son Oyisa Sidumo, thank you for being such a good boy and for understanding.  \niii  \nAuthorship  \nAll the work presented in this thesis is my own. I was involved in all aspects of this thesis which include implementation of all algorithms, data analysis and manuscript writing (lead author), though l benefited from the comments of my supervisors.  \nPart of the thesis have been presented at the following conferences:  \n❼ Sidumo B. , Sonono E. , and Takaidza I. 2021. “Count Regression and Machine Learning Techniques for Zero-Inflated Overdispersed Count Data: Application to Ecological Data”. Paper presented at the International Statistical Ecology Conference (ISEC 2022) Cape Town, 27 June-1 July 2022, University of Cape Town, South Africa.  \n❼ Sidumo B. , Sonono E. , and Takaidza I. 2022. “An approach to multi-class imbalanced problem in ecology using machine learning”. Paper presented at South African Statistical Association, 63rd Annual Conference (SASA 2022), 28 November-3 December 2022, George, South Africa.  \nParts of the thesis have been submitted for publication as journal articles:  \n❼ Sidumo B. , Sonono E. , and Takaidza I. 2021. “Count Regression and Machine Learning Techniques for Zero-Inflated Overdispersed Count Data: Application to Ecological Data”, Annals of Data Science, p. 1-15. (Published)  \n❼ Sidumo B. , Sonono E. , and Takaidza I. 2022.“An approach to multi-class imbalanced problem in ecology using machine learning”, Ecological Informatics 71 , p. 101822. (Published)  \n❼ Sidumo B. , 2022 .“A review of ma","cbCaijVV27guF7Zp","https://ap.wps.com/l/cbCaijVV27guF7Zp","pdf",14640387,1,169,"English","en",105,"# Executive Summary\n## Chapter 2 Review\n## Chapter 3 Methods and Applications","[{\"question\":\"What problem does the thesis focus on in ecological studies?\",\"answer\":\"The thesis focuses on estimation challenges in ecological studies, particularly zero-inflated overdispersed count data that commonly arises in population studies.\"},{\"question\":\"Why is overdispersion assessment important for count data?\",\"answer\":\"Ignoring overdispersion can lead to incorrect parameter estimates, which may cause researchers to mistakenly treat predictors as biologically important.\"},{\"question\":\"How does the thesis use machine learning techniques?\",\"answer\":\"The thesis applies machine learning (ML) techniques and ML regression approaches to reduce or address overdispersion and improve estimation and predictive performance for ecological data.\"}]","Estimation of Missing Data in Ecological Studies Using Machine Learning Techniques | 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