[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127255-en":3,"doc-seo-127255-105":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},127255,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Characterizing the Complexity of Tabular Machine Learning Classification Problems - Thesis","Machine learning algorithms are commonly applied to classification problems on tabular datasets, which can be described using meta-features such as dataset size, feature types, and class distribution. This dissertation reviews meta-features including complexity measures tied to class ambiguity, data sparsity, and class-boundary intricacy. Experimental results show existing feature-based measures fail to accurately quantify complexity in synthetic multiclass datasets. It proposes the F5 measure and new probabilistic complexity measures, demonstrating improved estimates for noisy problems. It also computes complexity values for categorically encoded datasets, identifies encoder choices producing least-complex datasets, selects meta-feature subsets predicting classifier performance, and uses regression plus statistical tests to validate predictions using either complexity or landmarking meta-features.","Characterizing the Complexity of Tabular Machine Learning Classification Problems  \nby  \nKyle Harper Erwin  \nThesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in the Faculty of Science at  \nStellenbosch University  \nSupervisor: Prof. A. Engelbrecht  \nMarch 2025  \nDeclaration  \nBy submitting this thesis electronically, I declare that the entirety of the work contained therein is my own, original work, that I am the sole author thereof (save to the extent explicitly otherwise stated), that reproduction and publication thereof by Stellenbosch University will not infringe any third party rights and that I have not previously in its entirety or in part submitted it for obtaining any qualification.  \nDate:  2023/0.2/ 10    \nCopyright © 2025 Stellenbosch University All rights reserved.  \nAbstract  \nCharacterizing the Complexity of Tabular Machine Learning Classification Problems  \nKyle Harper Erwin  \nDepartment of Computer Science,  \nUniversity of Stellenbosch,  \nBanghoek Rd & Joubert Rd, Stellenbosch, 7600, South Africa.  \nThesis: PhD (CS)  \nMarch 2025  \nMachine learning algorithms are commonly applied to classification problems on tabular datasets. These datasets can be described by their meta-features, such as the number of instances, feature types, and class distribution. Additionally, there exists a subcategory of meta-features, referred to as complexity measures, which estimate the difficulty of a tabular classification problem based on factors such as class ambiguity, data sparsity, and the intricacy of class boundaries. This dissertation conducts a thorough review of the meta-features, including complexity measures and other categories of meta-features. Experimental analysis on existing feature-based complexity measures show that they are inadequate for accurate quantification of the complexity of synthetic multiclass classification d atasets. A n ew m easure, c alled t he F 5 m easure, i s proposed, which evaluates the discriminative power of features for each class and better represents feature complexity for the same synthetic multi-class classification d atasets. A dditionally, a n ew c ategory o f p robabilistic complexity measures is also proposed, along with two new probabilistic measures. Experimental analysis shows that these measures complement existing measures and provide more accurate complexity estimates for noisy classification problems. The complexity values of categorically encoded datasets, specifically datasets with nominal data, are computed and statistical tests are used to determine which categorical encoder produces the least complex datasets. The subset of meta-features necessary to predict the performance of a classifier is identified using statistical and experimental analysis. This analysis required optimization of the hyperparameters of several algorithms for 222 tabular classification  \nABSTRACT iii  \nproblems. Furthermore, over 300 meta-features are computed for each classification problem. Regression experiments on the computed meta-features paired with algorithm test performance show that using either the identified subset of complexity meta-features or landmarking meta-features is sufficient to predict algorithm performance.  \nUittreksel  \nCharacterizing the Complexity of Tabular Machine Learning Classification Problems  \nKyle Harper Erwin  \nDepartment of Computer Science,  \nUniversity of Stellenbosch,  \nBanghoek Rd & Joubert Rd, Stellenbosch, 7600, South Africa.  \nTesis: PhD (CS)  \nMarch 2025  \nMasjienleer algoritmes word dikwels op klassifikasie probleme met tabel datastelle toegepas. Hierdie datastelle kan deur hul meta-kenmerke, soos die aantal gevalle, die eienskap tipes, en klas-verdeling beskryf word. Daar is ook ’n subkategorie van meta-kenmerke wat na verwys word as kompleksiteitsmaatstawwe, wat die moeilikheidsgraad van ’n tabelvorm klassifikasieprobleem skat gebaseer op klas-onduidelikheid, data-ylheid, en die verwikkeldheid van die kl","cbCais2DxOwooDVd","https://ap.wps.com/l/cbCais2DxOwooDVd","pdf",21827421,1,159,"English","en",105,"# Abstract\n## Meta-features and complexity measures\n## Proposed F5 and probabilistic measures\n## Experiments on encoded datasets and noisy classification\n## Meta-feature selection and performance prediction","[{\"question\":\"What are meta-features and complexity measures in tabular classification problems?\",\"answer\":\"Meta-features summarize properties of tabular datasets, such as instance count, feature types, and class distribution. Complexity measures are a subcategory that estimates difficulty using factors like class ambiguity, sparsity, and class-boundary intricacy.\"},{\"question\":\"Why are existing feature-based complexity measures considered inadequate?\",\"answer\":\"Experiments show they do not provide accurate complexity quantification for synthetic multiclass classification datasets.\"},{\"question\":\"What new methods does the dissertation propose for measuring complexity?\",\"answer\":\"It proposes the F5 measure to evaluate the discriminative power of features per class, and it introduces a new category of probabilistic complexity measures with two new probabilistic measures.\"}]","Characterizing the Complexity of Tabular Machine Learning Classification Problems - Thesis | PDF",1785937774,401,{"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},"characterizing-the-complexity-of-tabular-machine-learning-classification-problems-thesis","",{"@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/characterizing-the-complexity-of-tabular-machine-learning-classification-problems-thesis/127255/",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","2026-08-22","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 are meta-features and complexity measures in tabular classification problems?","Question",{"text":76,"@type":77},"Meta-features summarize properties of tabular datasets, such as instance count, feature types, and class distribution. Complexity measures are a subcategory that estimates difficulty using factors like class ambiguity, sparsity, and class-boundary intricacy.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Why are existing feature-based complexity measures considered inadequate?",{"text":81,"@type":77},"Experiments show they do not provide accurate complexity quantification for synthetic multiclass classification datasets.",{"name":83,"@type":74,"acceptedAnswer":84},"What new methods does the dissertation propose for measuring complexity?",{"text":85,"@type":77},"It proposes the F5 measure to evaluate the discriminative power of features per class, and it introduces a new category of probabilistic complexity measures with two new probabilistic measures.","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"]