[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126056-en":3,"doc-seo-126056-105":31,"detail-sidebar-cat-0-en-105":93},{"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},126056,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Human-Centered Machine Learning with Interpretable Visual Knowledge Discovery - Thesis","This research advances interpretable machine learning by introducing hyperblocks (HBs) as a structured, rule-based approach for building transparent and accurate models using numeric attributes meaningful to end users. Parallel Hyperblock Creation, Interactive Hyperblock Creation, Level n Hyperblock Creation, and k-nearest-neighbor hyperblock methods form a framework supported by lossless visualizations via General Line Coordinates (GLC). Case studies on Wisconsin Breast Cancer and MNIST show comparable accuracy on high-risk classification while maintaining interpretability that standard models struggle to match. The Visual Knowledge Discovery (VKD) process enables real-time parameter adjustment by domain experts, supporting trusted, human-centered collaboration.","Central Washington University  \nScholarWorks@CWU  \n\n| All Master's Theses | Master's Theses |\n| --- | --- |\n| Fall 2024\u003Cbr>Human-Centered Machine Learning with Interpretable Visual Knowledge Discovery\u003Cbr>Lincoln Huber\u003Cbr>[huberlw1@gmail.com](huberlw1@gmail.com)\u003Cbr>Follow this and additional works at: [https://digitalcommons.cwu.edu/etd](https://digitalcommons.cwu.edu/etd)\u003Cbr> Part of the Artificial Intelligence and Robotics Commons, and the Graphics and Human Computer Interfaces Commons |  |\n\nRecommended Citation  \nHuber, Lincoln, \"Human-Centered Machine Learning with Interpretable Visual Knowledge Discovery\"(2024) . All Master 's Theses. 1991.  \n[https://digitalcommons.cwu.edu/etd/1991](https://digitalcommons.cwu.edu/etd/1991)  \nThis Thesis is brought to you for free and open access by the Master's Theses at ScholarWorks@CWU. It has been accepted for inclusion in All Master's Theses by an authorized administrator of ScholarWorks@CWU. For more information, please contact [scholarworks@cwu.edu](scholarworks@cwu.edu).  \nHUMAN-CENTERED MACHINE LEARNING WITH INTERPRETABLE  \nVISUAL KNOWLEDGE DISCOVERY  \n\n| A Thesis\u003Cbr>Presented to The Graduate Faculty Central Washington University |\n| --- |\n| In Partial Fulfillment\u003Cbr>of the Requirements for the Degree Master of Science Computational Science |\n\nby  \nLincoln Will Huber  \nNovember 2024  \nCENTRAL WASHINGTON UNIVERSITY  \nGraduate Studies  \nWe hereby approve the thesis of  \nLincoln Will Huber  \nCandidate for the degree of Master of Science  \nAPPROVED FOR THE GRADUATE FACULTY  \n\n|  |\n| --- |\n|  |\n|  |\n\n\n| Dr. Boris Kovalerchuk, Committee Chair |\n| --- |\n| Dr. Razvan Andonie, Committee Member |\n| Dr. Szilard VAJDA, Committee Chair |\n\nDean of Graduate Studies  \nABSTRACT  \nHUMAN-CENTERED MACHINE LEARNING WITH INTERPRETABLE VISUAL  \nKNOWLEDGE DISCOVERY  \nby  \nLincoln Will Huber  \nNovember 2024  \nThis research advances interpretable machine learning (ML) by introducing hyperblocks (HBs) as a structured, rule-based approach for creating transparent and accurate models using meaningful numeric attributes directly interpretable to end users. Key techniques, including Parallel Hyperblock Creation, Interactive Hyperblock Creation, Level n Hyperblock Creation, and k-Nearest Neighbor Hyperblock, provide a framework that ensures domain experts can meaningfully engage with the model’s decision-making process through lossless visualizations using General Line Coordinates (GLC) . Case studies with the Wisconsin Breast Cancer and MNIST datasets demonstrated HBs' effectiveness in handling high-risk and complex classification tasks, offering interpretable accuracy that traditional models struggle to achieve. In the Wisconsin Breast Cancer study, HBs achieved an accuracy comparable to standard ML methods while providing an interpretable framework for cancer diagnosis, where model trust is critical. In MNIST, HBs showed their ability to scale to larger datasets while  \nmaintaining a high level of interpretability. Finally, the Visual Knowledge Discovery (VKD) process, central to this approach, allows experts to adjust model parameters in real time, promoting human-centered insights and collaboration. Overall, this work presents HBs and VKD as powerful tools for interpretable, high-stakes ML applications, supporting transparency, accuracy, and domain relevance.  \nACKNOWLEDGMENTS  \nFirst and foremost, I would like to give thanks to my parents and brother for helping and supporting me throughout my education. Without your love and support I would not be where I am now.  \nI would also like to express my gratitude to Dr. Boris Kovalerchuk for granting me a position in his Visual Knowledge Discovery lab and introducing me to such an important field of study. Your insight and advice throughout the years have been invaluable.  \nFurthermore, I would like to thank Dr. Szilard VAJDA for first suggesting I pursue graduate studies. Your encouraging words and exceptional teaching have helped me significantly.  \nAdditional","cbCaibdEja6P1W8k","https://ap.wps.com/l/cbCaibdEja6P1W8k","pdf",4036938,7,1,82,"English","en",105,"# Introduction\n# Literature Review\n# Concepts\n## Background Concepts\n## Proposed Concepts\n# Methods\n## Parallel Hyperblock Creation\n## Interactive Hyperblock Creation","[{\"question\":\"What are hyperblocks (HBs) in this thesis?\",\"answer\":\"Hyperblocks are a structured, rule-based method for creating interpretable machine learning models using numeric attributes that end users can understand. They are designed to support transparent decision-making and accurate modeling.\"},{\"question\":\"How does the approach achieve interpretability and visualization?\",\"answer\":\"The framework uses General Line Coordinates (GLC) to produce lossless visualizations tied to the hyperblock structure. This lets domain experts inspect and understand model behavior.\"},{\"question\":\"What datasets were used to evaluate the proposed method?\",\"answer\":\"The thesis evaluates hyperblocks using the Wisconsin Breast Cancer dataset and MNIST. Results show interpretable accuracy for high-risk classification and scaling to larger datasets while preserving interpretability.\"}]","Human-Centered Machine Learning with Interpretable Visual Knowledge Discovery - Thesis | PDF",1785902817,207,{"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":88,"head_meta":90,"extra_data":92,"updated_unix":29},"human-centered-machine-learning-with-interpretable-visual-knowledge-discovery-thesis","",{"@graph":37,"@context":87},[38,55,70],{"@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":54},"https://docshare.wps.com/document/human-centered-machine-learning-with-interpretable-visual-knowledge-discovery-thesis/126056/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What are hyperblocks (HBs) in this thesis?","Question",{"text":77,"@type":78},"Hyperblocks are a structured, rule-based method for creating interpretable machine learning models using numeric attributes that end users can understand. They are designed to support transparent decision-making and accurate modeling.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the approach achieve interpretability and visualization?",{"text":82,"@type":78},"The framework uses General Line Coordinates (GLC) to produce lossless visualizations tied to the hyperblock structure. This lets domain experts inspect and understand model behavior.",{"name":84,"@type":75,"acceptedAnswer":85},"What datasets were used to evaluate the proposed method?",{"text":86,"@type":78},"The thesis evaluates hyperblocks using the Wisconsin Breast Cancer dataset and MNIST. 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