[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127971-en":3,"doc-seo-127971-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127971,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Mitigating learning biases - A study on uncertainty quantification and instantial variability in machine learning models","Machine learning (ML) enables advanced healthcare solutions, yet uncertainty and data/model limitations can undermine reliability. This thesis investigates how instantial variability affects accuracy and robustness of ML models in EEG microstate analysis, where brief brain activity patterns are used to study neural dynamics. Resting-state EEG recordings from 203 participants were analyzed using features such as Global Explained Variance, Mean Durations, and Corrected Time Coverage. Probabilistic augmentation and uncertainty-aware classification were evaluated with KNN, ACS, ACG, WSF, and compared to LSVM under baseline and perturbed conditions. Results highlight the value of variability-aware techniques for more robust clinical diagnostics and brain-computer interfaces, motivating future work on larger datasets and real-time methods.","Master Thesis in ICT FOR INTERNET AND MULTIMEDIA  \nMitigating learning biases: A study on uncertainty quantification and instantial variability in machine  \nlearning models  \nMaster Candidate Supervisor  \nAlda Kola Prof. Roberto Corvaja  \nStudent ID 2071530 University of Padova  \nCo-supervisor  \nProf. Giulia Cisotto University of Trieste  \nAcademic Year: 2023/2024  \nGraduation Date: December, 4th 2024  \nTo my family.  \nTo my parents, Valentin and Dila, to all their sacrifices.  \nTo my sister Florinda,, my absolute favorite person, who has always been my light.  \nAbstract  \nMachine learning (ML) has become a significant driver of advancement in healthcare, enabling advanced solutions for diagnostics, personalized treatments, and decision-making support. In the study of brain dynamics, EEG microstate analysis stands out as a valuable technique for exploring brain activity, offering insights into cognitive functions and neural processes. However, inherent data variability and model limitations introduce uncertainty, posing significant challenges to the reliability of ML applications in this domain.  \nThis thesis explores the impact of instantial variability on the accuracy and robustness of machine learning (ML) models in EEG microstate analysis. EEG microstates, brief patterns of brain activity, are crucial for understanding neural dynamics. Using a dataset of resting-state EEG recordings from203participants, key microstate features such as Global Explained Variance, Mean Durations, and Corrected Time Coverage were analyzed.  \nTo simulate variability, probabilistic augmentation techniques were applied on the dataset and uncertainty-aware methods were used to classify microstates. Four classifiers: K-Nearest Neighbors (KNN), Augmented Support Vector Classifier (ACS), Augmented Gradient Boosting Classifier (ACG), and Weighted Sampling Forest (WSF) were evaluated under baseline and perturbed conditions and compared with the performance of a traditional ML model, Linear Support Vector Machine (LSVM). The ACS model consistently showed the highest performance, demonstrating the effectiveness of augmentation and uncertainty quantification in enhancing robustness. To further evaluate the robustness of these classifiers, perturbations were introduced to simulate real-world variability.  \nThe findings emphasize the importance of variability-aware techniques in improving ML models for EEG analysis, paving the way for more reliable applications in clinical diagnostics and brain-computer interfaces. Future work should focus on expanding datasets, exploring deep learning approaches, and adapting methods to real-time applications.  \nContents  \nList of Figures xi  \nList of Tables xiii  \nList of Acronyms xix  \n1 Introduction 1  \n2 Background 5  \n2.1 Uncertainty Quantification Methods ................. 7  \n2.2 Instantial Variability .......................... 8  \n2.3 EEG Microstate Analysis ........................ 10  \n3 Methods 13  \n3.1 Dataset Description ........................... 14  \n3.1.1 Preprocessing .......................... 14  \n3.1.2 Microstates and Their Classification . . . . . . . . . . . . . 15  \n3.2 Simulating Variability in Microstates: Artificial Perturbations of Microstate Features ........................... 16  \n3.2.1 Simulating Perturbations ................... 16  \n3.2.2 Quantifying Perturbations ................... 17  \n3.3 Classification Models with Implementation ............. 19  \n3.4 Assessing the Impact of Variability on Classification ........ 21  \n3.4.1 Microstates and their artificial perturbation ......... 22  \n3.4.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . 23  \n4 Results and Discussions 25  \n4.1 Variability Analysis ........................... 26  \n4.2 The Impact of Variability on Classification .............. 30  \nCONTENTS  \n4.2. 1 Classification Performance with no Perturbations . . . . . 31  \n4.3 Performance Degradation under Perturbations ........... 35  \n4.3.1 Comparative Analysis of Models ..........","cbCairynmDOZlNOj","https://ap.wps.com/l/cbCairynmDOZlNOj","pdf",12206231,4,1,65,"English","en",105,"# 1 Introduction\n# 2 Background\n## 2.1 Uncertainty Quantification Methods\n## 2.2 Instantial Variability\n## 2.3 EEG Microstate Analysis\n# 3 Methods\n## 3.1 Dataset Description\n## 3.1.1 Preprocessing\n## 3.1.2 Microstates and Their Classification\n## 3.2 Simulating Variability in Microstates: Artificial Perturbations of Microstate Features\n## 3.2.1 Simulating Perturbations\n## 3.2.2 Quantifying Perturbations\n## 3.3 Classification Models with Implementation\n## 3.4 Assessing the Impact of Variability on Classification\n## 3.4.1 Microstates and their artificial perturbation\n## 3.4.2 Evaluation Metrics\n# 4 Results and Discussions\n## 4.1 Variability Analysis\n## 4.2 The Impact of Variability on Classification\n## 4.2.1 Classification Performance with no Perturbations\n## 4.3 Performance Degradation under Perturbations\n## 4.3.1 Comparative Analysis of Models\n# 5 Conclusions and Future Works","[{\"question\":\"What is the focus of the thesis on learning biases?\",\"answer\":\"The thesis studies how learning biases manifest through uncertainty and instantial variability, and how these factors impact ML performance in EEG microstate analysis.\"},{\"question\":\"Which EEG microstate features and data are used in the study?\",\"answer\":\"The work analyzes resting-state EEG recordings from 203 participants and uses microstate features including Global Explained Variance, Mean Durations, and Corrected Time Coverage.\"},{\"question\":\"How is variability simulated and how are classifiers evaluated?\",\"answer\":\"Probabilistic augmentation and uncertainty-aware methods classify microstates, with perturbations introduced to simulate real-world variability. KNN, ACS, ACG, and WSF are compared against a baseline LSVM model under both baseline and perturbed conditions.\"}]","Mitigating learning biases - A study on uncertainty quantification and instantial variability in machine learning models | PDF",1785943476,164,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"mitigating-learning-biases-a-study-on-uncertainty-quantification-and-instantial-variability-in-machine-learning-models","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/mitigating-learning-biases-a-study-on-uncertainty-quantification-and-instantial-variability-in-machine-learning-models/127971/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",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 focus of the thesis on learning biases?","Question",{"text":76,"@type":77},"The thesis studies how learning biases manifest through uncertainty and instantial variability, and how these factors impact ML performance in EEG microstate analysis.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which EEG microstate features and data are used in the study?",{"text":81,"@type":77},"The work analyzes resting-state EEG recordings from 203 participants and uses microstate features including Global Explained Variance, Mean Durations, and Corrected Time Coverage.",{"name":83,"@type":74,"acceptedAnswer":84},"How is variability simulated and how are classifiers evaluated?",{"text":85,"@type":77},"Probabilistic augmentation and uncertainty-aware methods classify microstates, with perturbations introduced to simulate real-world variability. KNN, ACS, ACG, and WSF are compared against a baseline LSVM model under both baseline and perturbed conditions.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]