{"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/fast-meta-learning-for-adaptive-hierarchical","title":"Fast Meta-Learning for Adaptive Hierarchical Classifier Design","arxiv_id":"1711.03512","date":"2017-11-09","proceeding":null,"authors":["Gerrit J. J. van den Burg","Alfred O. Hero"],"abstract":"We propose a new splitting criterion for a meta-learning approach to\nmulticlass classifier design that adaptively merges the classes into a\ntree-structured hierarchy of increasingly difficult binary classification\nproblems. The classification tree is constructed from empirical estimates of\nthe Henze-Penrose bounds on the pairwise Bayes misclassification rates that\nrank the binary subproblems in terms of difficulty of classification. The\nproposed empirical estimates of the Bayes error rate are computed from the\nminimal spanning tree (MST) of the samples from each pair of classes. Moreover,\na meta-learning technique is presented for quantifying the one-vs-rest Bayes\nerror rate for each individual class from a single MST on the entire dataset.\nExtensive simulations on benchmark datasets show that the proposed hierarchical\nmethod can often be learned much faster than competing methods, while achieving\ncompetitive accuracy.","url_abs":"http://arxiv.org/abs/1711.03512v1","url_pdf":"http://arxiv.org/pdf/1711.03512v1.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":"fast-meta-learning-for-adaptive-hierarchical","repo_url":"https://github.com/HeroResearchGroup/SmartSVM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"fast-meta-learning-for-adaptive-hierarchical","repo_url":"https://github.com/codelion/adaptive-classifier","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}