{"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/inverse-category-frequency-based-supervised","title":"Inverse-Category-Frequency based supervised term weighting scheme for text categorization","arxiv_id":"1012.2609","date":"2010-12-13","proceeding":null,"authors":["Deqing Wang","HUI ZHANG"],"abstract":"Term weighting schemes often dominate the performance of many classifiers,\nsuch as kNN, centroid-based classifier and SVMs. The widely used term weighting\nscheme in text categorization, i.e., tf.idf, is originated from information\nretrieval (IR) field. The intuition behind idf for text categorization seems\nless reasonable than IR. In this paper, we introduce inverse category frequency\n(icf) into term weighting scheme and propose two novel approaches, i.e., tf.icf\nand icf-based supervised term weighting schemes. The tf.icf adopts icf to\nsubstitute idf factor and favors terms occurring in fewer categories, rather\nthan fewer documents. And the icf-based approach combines icf and relevance\nfrequency (rf) to weight terms in a supervised way. Our cross-classifier and\ncross-corpus experiments have shown that our proposed approaches are superior\nor comparable to six supervised term weighting schemes and three traditional\nschemes in terms of macro-F1 and micro-F1.","url_abs":"http://arxiv.org/abs/1012.2609v4","url_pdf":"http://arxiv.org/pdf/1012.2609v4.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":"inverse-category-frequency-based-supervised","repo_url":"https://github.com/textvec/textvec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"inverse-category-frequency-based-supervised","repo_url":"https://github.com/zveryansky/textvec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"cross-corpus","task_name":"Cross-corpus"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"text-categorization","task_name":"Text Categorization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-class-classification-on-reuters-52","task":"Multi-class Classification","dataset":"Reuters-52","model":"SVM (tficf)","rank_in_archive_order":1,"of":1,"metrics":{"Macro F1":"73.9"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}