{"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-abc-boost-a-unified-framework-for","title":"Fast ABC-Boost: A Unified Framework for Selecting the Base Class in Multi-Class Classification","arxiv_id":"2205.10927","date":"2022-05-22","proceeding":null,"authors":["Ping Li","Weijie Zhao"],"abstract":"The work in ICML'09 showed that the derivatives of the classical multi-class logistic regression loss function could be re-written in terms of a pre-chosen \"base class\" and applied the new derivatives in the popular boosting framework. In order to make use of the new derivatives, one must have a strategy to identify/choose the base class at each boosting iteration. The idea of \"adaptive base class boost\" (ABC-Boost) in ICML'09, adopted a computationally expensive \"exhaustive search\" strategy for the base class at each iteration. It has been well demonstrated that ABC-Boost, when integrated with trees, can achieve substantial improvements in many multi-class classification tasks. Furthermore, the work in UAI'10 derived the explicit second-order tree split gain formula which typically improved the classification accuracy considerably, compared with using only the fist-order information for tree-splitting, for both multi-class and binary-class classification tasks. In this paper, we develop a unified framework for effectively selecting the base class by introducing a series of ideas to improve the computational efficiency of ABC-Boost. Our framework has parameters $(s,g,w)$. At each boosting iteration, we only search for the \"$s$-worst classes\" (instead of all classes) to determine the base class. We also allow a \"gap\" $g$ when conducting the search. That is, we only search for the base class at every $g+1$ iterations. We furthermore allow a \"warm up\" stage by only starting the search after $w$ boosting iterations. The parameters $s$, $g$, $w$, can be viewed as tunable parameters and certain combinations of $(s,g,w)$ may even lead to better test accuracy than the \"exhaustive search\" strategy. Overall, our proposed framework provides a robust and reliable scheme for implementing ABC-Boost in practice.","url_abs":"https://arxiv.org/abs/2205.10927v2","url_pdf":"https://arxiv.org/pdf/2205.10927v2.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-abc-boost-a-unified-framework-for","repo_url":"https://github.com/pltrees/abcboost","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"}],"methods":[{"method_slug":"base","method_name":"BASE"},{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2205.10927","atlas_url":"https://app.syntology.ai/?focus=2205.10927","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.10927"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/pltrees/abcboost","reach":null}],"summary":{"unverified":1},"by_repo_kind":{"listed":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"af0168416cc75d77","entry":"train","repo":"pltrees/abcboost","repo_kind":"listed","path":"src/abcboost.py","file_url":"https://github.com/pltrees/abcboost/blob/HEAD/src/abcboost.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"af0168416cc75d77"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}