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Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>\n            Fair feature selection for classification decision tasks has recently garnered significant attention from researchers. However, existing fair feature selection algorithms fall short of providing a full explanation of the causal relationship between features and sensitive attributes, potentially impacting the accuracy of fair feature identification. To address this issue, we propose a fair causal feature selection algorithm, called\n            <jats:italic>FairCFS<\/jats:italic>\n            . Specifically, FairCFS constructs a localized causal graph that identifies the Markov blankets of class and sensitive variables, to block the transmission of sensitive information for selecting fair causal features. Extensive experiments on seven public real-world datasets validate that FairCFS has accuracy comparable to eight state-of-the-art feature selection algorithms while presenting more superior fairness.\n          <\/jats:p>","DOI":"10.1145\/3643890","type":"journal-article","created":{"date-parts":[[2024,2,3]],"date-time":"2024-02-03T11:45:02Z","timestamp":1706960702000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Fair Feature Selection: A Causal Perspective"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4812-6676","authenticated-orcid":false,"given":"Zhaolong","family":"Ling","sequence":"first","affiliation":[{"name":"Anhui University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4068-8126","authenticated-orcid":false,"given":"Enqi","family":"Xu","sequence":"additional","affiliation":[{"name":"Anhui University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3675-4985","authenticated-orcid":false,"given":"Peng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Anhui University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3294-5071","authenticated-orcid":false,"given":"Liang","family":"Du","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Shanxi University, Taiyuan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2442-4572","authenticated-orcid":false,"given":"Kui","family":"Yu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), and the School of Computer Science and Information Technology, Hefei University of Technology, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2396-1704","authenticated-orcid":false,"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), and the School of Computer Science and Information Technology, Hefei University of Technology, Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"issue":"7","key":"e_1_3_2_2_2","article-title":"Local causal and Markov blanket induction for causal discovery and feature selection for classification part I: algorithms and empirical evaluation.","volume":"11","author":"Aliferis Constantin F.","year":"2010","unstructured":"Constantin F. 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