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This paper investigates large-scale anomaly detection based on the Isolation Forest algorithm, enhancing the algorithm\u2019s performance in the context of big data by introducing the method of adaptive feature selection. The proposed approach is a fusion of the Isolation Forest and adaptive feature selection, dynamically adjusting feature weights to adapt more flexibly to the contributions of different features. Experimental results on large-scale datasets demonstrate that adaptive feature selection significantly improves the anomaly detection performance of the Isolation Forest algorithm. This method provides a new perspective for enhancing anomaly detection techniques and addressing the challenges posed by large-scale, high-dimensional data. Its practical implications are crucial for real-world applications.<\/jats:p>","DOI":"10.1177\/14727978251337984","type":"journal-article","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T08:41:26Z","timestamp":1745570486000},"page":"4143-4154","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Anomaly detection algorithm for big data based on isolation forest algorithm"],"prefix":"10.1177","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1094-1847","authenticated-orcid":false,"given":"Min","family":"He","sequence":"first","affiliation":[{"name":"School of Intelligent Manufacturing and Information Engineering, Ya\u2019an Polytechnic College, Ya\u2019an, China"}]},{"given":"Xiang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Intelligent Manufacturing and Information Engineering, Ya\u2019an Polytechnic College, Ya\u2019an, China"}]}],"member":"179","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"issue":"3","key":"e_1_3_3_2_2","first-page":"829","article-title":"A comprehensive survey of anomaly detection algorithms","volume":"10","author":"Samariya D","year":"2023","unstructured":"Samariya D, Thakkar A. 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