{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/binary-classification-in-unstructured-space","title":"Binary Classification in Unstructured Space With Hypergraph Case-Based Reasoning","arxiv_id":"1806.06232","date":"2018-06-16","proceeding":null,"authors":["Alexandre Quemy"],"abstract":"Binary classification is one of the most common problem in machine learning.\nIt consists in predicting whether a given element belongs to a particular\nclass. In this paper, a new algorithm for binary classification is proposed\nusing a hypergraph representation. The method is agnostic to data\nrepresentation, can work with multiple data sources or in non-metric spaces,\nand accommodates with missing values. As a result, it drastically reduces the\nneed for data preprocessing or feature engineering. Each element to be\nclassified is partitioned according to its interactions with the training set.\nFor each class, a seminorm over the training set partition is learnt to\nrepresent the distribution of evidence supporting this class.\n  Empirical validation demonstrates its high potential on a wide range of\nwell-known datasets and the results are compared to the state-of-the-art. The\ntime complexity is given and empirically validated. Its robustness with regard\nto hyperparameter sensitivity is studied and compared to standard\nclassification methods. Finally, the limitation of the model space is\ndiscussed, and some potential solutions proposed.","url_abs":"http://arxiv.org/abs/1806.06232v3","url_pdf":"http://arxiv.org/pdf/1806.06232v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"binary-classification-in-unstructured-space","repo_url":"https://github.com/aquemy/HCBR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"missing-values","task_name":"Missing Values"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}