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Data"],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>Feature selection is an essential pre-process component in data mining that aims to select the most relevant features from the target dataset. Datasets are always dynamic in real-world applications, and features may exist in stream mode. Then, online streaming feature selection methods are proposed, which deal with streaming features arriving continuously in real-time. However, most existing algorithms prioritize high accuracy and low time-consumption but overlook the stability of the selected features. Stable feature selection results are crucial for users in practice. For instance, in the medical field, unstable feature selection results can make it challenging for experts to identify the main causative factors of a disease. Motivated by this, this article proposes a new online stable streaming feature selection method via feature aggregation named OSSFS. Specifically, inspired by the cohesive MeanShift approach, OSSFS applies an incremental aggregation strategy to partition the streaming features into multiple hyperellipsoids. Then, we incrementally update and merge these hyperellipsoids with new streaming features. Finally, we select representative features from each hyperellipsoid as the final selected feature subset. Extensive experiments are conducted on several real-world datasets to compare our new method with state-of-the-art competing algorithms in cases of stability and predictive accuracy. Experimental results indicate that OSSFS achieves optimal stability without losing prediction accuracy.<\/jats:p>","DOI":"10.1145\/3715918","type":"journal-article","created":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T16:24:36Z","timestamp":1738340676000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Online Stable Streaming Feature Selection via Feature Aggregation"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3607-0022","authenticated-orcid":false,"given":"Peng","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3773-5955","authenticated-orcid":false,"given":"Qi","family":"Wang","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3768-2610","authenticated-orcid":false,"given":"Yunyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4812-6676","authenticated-orcid":false,"given":"Zhaolong","family":"Ling","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7647-3603","authenticated-orcid":false,"given":"Shu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2396-1704","authenticated-orcid":false,"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,3,10]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"356","volume-title":"Proceedings of the 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI \u201912)","author":"Awada Wael","year":"2012","unstructured":"Wael Awada, Taghi M. 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