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(b) They are being disadvantaged when mislabeled samples are located in overlapping areas of different classes. Here, we develop an integrated architecture\u00a0\u2013 self-training algorithm based on density peaks combining globally adaptive multi-local noise filter (STDP-GAMLNF), to improve detecting efficiency. Firstly, the spatial structure of the data set is revealed by density peak clustering, and it is used for empowering self-training to label unlabeled samples. In the meantime, after each epoch of labeling, GAMLNF can comprehensively judge whether a sample is a mislabeled sample from multiple classes or not, and it will reduce the influence of edge samples effectively. The corresponding experimental results conducted on eighteen UCI data sets demonstrate that GAMLNF is not sensitive to the value of the neighbor parameter k, and it is capable of adaptively finding the appropriate number of neighbors of each class.<\/jats:p>","DOI":"10.3233\/ida-226575","type":"journal-article","created":{"date-parts":[[2023,4,7]],"date-time":"2023-04-07T11:37:28Z","timestamp":1680867448000},"page":"323-343","source":"Crossref","is-referenced-by-count":1,"title":["Self-training algorithm based on density peaks combining globally adaptive multi-local noise filter"],"prefix":"10.1177","volume":"27","author":[{"given":"Shuaijun","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"12","key":"10.3233\/IDA-226575_ref1","first-page":"73","article-title":"disentangled variational auto-encoder for semi-supervised learning","volume":"482","author":"Li","year":"2019","journal-title":"Information Sciences"},{"key":"10.3233\/IDA-226575_ref2","doi-asserted-by":"crossref","unstructured":"C. Yuan et al., semi-supervised stacked autoencoder-based deep hierarchical semantic feature for real-time fingerprint liveness detection, Journal of Real-Time Image Processing 17(1) (2020), 55\u201371.","DOI":"10.1007\/s11554-019-00928-0"},{"key":"10.3233\/IDA-226575_ref3","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.knosys.2017.06.023","article-title":"A combination of active learning and self-learning for named entity recognition on twitter using conditional random fields","volume":"132","author":"Tran","year":"2017","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/IDA-226575_ref4","first-page":"10","article-title":"boosting semi-supervised face recognition with noise robustness","volume":"99","author":"Liu","year":"2021","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"10.3233\/IDA-226575_ref5","doi-asserted-by":"crossref","unstructured":"M. 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Pei et al., A threshold-free classification mechanism in genetic programming for high-dimensional unbalanced classification, in: IEEE Congress on Evolutionary Computation, 2020, pp.\u00a01\u20138.","DOI":"10.1109\/CEC48606.2020.9185503"}],"container-title":["Intelligent Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDA-226575","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:20:00Z","timestamp":1777454400000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDA-226575"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,15]]},"references-count":29,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/ida-226575","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"value":"1088-467X","type":"print"},{"value":"1571-4128","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,15]]}}}