[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118926-en":3,"doc-seo-118926-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},118926,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Evaluation of Machine Learning Methods for Multivariate Classification with Application to Environmental Datasets","As environmental data grows in complexity, machine learning offers a route to derive actionable insights. This thesis evaluates ten machine learning models for multi-class classification in multivariate settings, emphasizing complex environmental data, specifically polycyclic aromatic hydrocarbons (PAHs). Simulation studies compare predictive performance under varying correlation structures among independent variables. Results indicate Regularized Multinomial Logistic Regression excels when predictors are independent, while Gradient Boosting Machines perform best when predictors are highly correlated. Feature selection accuracy and sensitivity are also assessed across methods and data scenarios.","University of Memphis  \nUniversity of Memphis Digital Commons  \nElectronic Theses and Dissertations  \n7-20-2023  \nEvaluation of Machine Learning Methods for Multivariate Classification with Application to Environmental Datasets  \nXianqiang Fu  \nFollow this and additional works at: [https://digitalcommons.memphis.edu/etd](https://digitalcommons.memphis.edu/etd)  \nRecommended Citation  \nFu, Xianqiang, \"Evaluation of Machine Learning Methods for Multivariate Classification with Application to Environmental Datasets\" (2023) . Electronic Theses and Dissertations. 3012.  \n[https://digitalcommons.memphis.edu/etd/3012](https://digitalcommons.memphis.edu/etd/3012)  \nThis Thesis is brought to you for free and open access by University of Memphis Digital Commons. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of University of Memphis Digital Commons. For more information, please contact [khggerty@memphis.edu](khggerty@memphis.edu).  \nEVALUATION OF MACHINE LEARNING METHODS FOR MULTIVARIATE CLASSIFICATION WITH APPLICATION TO ENVIRONMENTAL DATASETS  \nby  \nXianqiang Fu  \nA Thesis  \nSubmitted in Partial Fulfillment of the  \nRequirements for the Degree of  \nMaster of Science  \nMajor: Biostatistics  \nThe University of Memphis  \nAugust 2023  \nAcknowledgments  \nI am sincerely grateful to my advisory committee, whose combined expertise, guidance, and unwavering support have made this work possible.  \nTo Dr. Joyce Jiang, my advisor, whose insightful perspectives and steady guidance have been pivotal to my journey. Our bi-weekly meetings have not only served as waypoints on the path of this thesis but have also helped shape my broader view of our topic. Your ability to demystify complex concepts and provide practical programming advice is something I deeply appreciate.  \nTo Dr. Hongmei Zhang, who was always ready with support and advice when I needed it. Your professional expertise and encouragement have been critical to my master's journey. Your guidance has lit the path for me in moments of uncertainty, for which I am truly grateful.  \nLastly, to Dr. Chunrong Jia, whose grant has allowed me to embark on this educational journey in Biostatistics. Your support and guidance from an environmental perspective have been invaluable in broadening my understanding and providing me with a holistic view of our work.  \nI want to acknowledge each of them for their mentorship, which has been a guiding light throughout this master's program, leading this academic work and shaping my path as a potential biostatistician. Thank you all.  \nAbstract  \nAs environmental data grows in complexity, machine learning presents an avenue to extract meaningful insights from such data. This study aimed to investigate the applicability and performance of various machine learning methods for multi-class classification problems, with a specific focus on complex environmental data, including Polycyclic Aromatic Hydrocarbons (PAHs) . In the current study, we evaluated ten machine learning models to assess their performance in multivariate classification problems using simulation studies. The results showed that Regularized Multinomial Logistic Regression (RMLR) has higher classification accuracy when the independent variables are independent, while the Gradient Boosting Machine (GBM) outperformed others when the independent variables are highly correlated. Furthermore, the feature selection accuracy of three different methods was also evaluated. GBM and Random Forest (RF) showed a higher sensitivity compared to other methods across different data settings. Based on these findings, it appears that linear models such as RMLR and MLR may not achieve optimal performance when confronted with highly correlated independent variables. Instead, tree-based methods, such as GBM and RF, prove to be a better choice. Overall, it is crucial to choose the appropriate machine learning methods based on the complexity of environmental data and ","cbCailU3N03cs67C","https://ap.wps.com/l/cbCailU3N03cs67C","pdf",1043060,1,46,"English","en",105,"# 1. Introduction\n# 2. Literature Review\n# 3. Machine Learning Methods\n## Linear Discriminant Analysis (LDA)\n## Multinomial Logistic Regression (MLR)\n## Regularized Multinomial Logistic Regression (RMLR)\n## Naïve Bayes Classifier (NBC)\n## Decision Tree (DT)\n## Random Forest (RF)\n## Gradient Boosting Machine (GBM)\n## K Nearest Neighbor (KNN)\n## Convolutional Neural Networks (CNN)\n# 4. Environmental Polycyclic Aromatic Hydrocarbons (PAHs) Data\n# 5. Objectives\n# 6. Simulation Study\n# 7. Simulation Results\n# 8. Discussions and Conclusions\n# 9. Future Work\n# References","[{\"question\":\"What is the main goal of this thesis?\",\"answer\":\"To evaluate the applicability and performance of multiple machine learning methods for multi-class multivariate classification using environmental datasets, with emphasis on PAH data.\"},{\"question\":\"How do model performances change with predictor correlation?\",\"answer\":\"Regularized Multinomial Logistic Regression performs better when independent variables are independent, whereas Gradient Boosting Machines outperform others when variables are highly correlated.\"},{\"question\":\"Which methods performed best in feature selection sensitivity?\",\"answer\":\"GBM and Random Forest show higher sensitivity than other methods across different data settings, according to the evaluated scenarios.\"}]","Evaluation of Machine Learning Methods for Multivariate Classification with Application to Environmental Datasets | PDF",1785720979,116,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"evaluation-of-machine-learning-methods-for-multivariate-classification-with-application-to-environmental-datasets","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/evaluation-of-machine-learning-methods-for-multivariate-classification-with-application-to-environmental-datasets/118926/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main goal of this thesis?","Question",{"text":76,"@type":77},"To evaluate the applicability and performance of multiple machine learning methods for multi-class multivariate classification using environmental datasets, with emphasis on PAH data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do model performances change with predictor correlation?",{"text":81,"@type":77},"Regularized Multinomial Logistic Regression performs better when independent variables are independent, whereas Gradient Boosting Machines outperform others when variables are highly correlated.",{"name":83,"@type":74,"acceptedAnswer":84},"Which methods performed best in feature selection sensitivity?",{"text":85,"@type":77},"GBM and Random Forest show higher sensitivity than other methods across different data settings, according to the evaluated scenarios.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]