{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T14:16:39Z","timestamp":1775312199615,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T00:00:00Z","timestamp":1728950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Jiangxi Province of China","award":["20242BAB26024"],"award-info":[{"award-number":["20242BAB26024"]}]},{"name":"Natural Science Foundation of Jiangxi Province of China","award":["YC2023-S865"],"award-info":[{"award-number":["YC2023-S865"]}]},{"name":"Natural Science Foundation of Jiangxi Province of China","award":["JXYJG-2022-172"],"award-info":[{"award-number":["JXYJG-2022-172"]}]},{"name":"Graduate Innovation Special Foundation of Jiangxi Province of China","award":["20242BAB26024"],"award-info":[{"award-number":["20242BAB26024"]}]},{"name":"Graduate Innovation Special Foundation of Jiangxi Province of China","award":["YC2023-S865"],"award-info":[{"award-number":["YC2023-S865"]}]},{"name":"Graduate Innovation Special Foundation of Jiangxi Province of China","award":["JXYJG-2022-172"],"award-info":[{"award-number":["JXYJG-2022-172"]}]},{"name":"Program of Academic Degree and Graduate Education and Teaching Reform in Jiangxi Province of China","award":["20242BAB26024"],"award-info":[{"award-number":["20242BAB26024"]}]},{"name":"Program of Academic Degree and Graduate Education and Teaching Reform in Jiangxi Province of China","award":["YC2023-S865"],"award-info":[{"award-number":["YC2023-S865"]}]},{"name":"Program of Academic Degree and Graduate Education and Teaching Reform in Jiangxi Province of China","award":["JXYJG-2022-172"],"award-info":[{"award-number":["JXYJG-2022-172"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Support vector regression (SVR) is a powerful kernel-based regression prediction algorithm that performs excellently in various application scenarios. However, for real-world data, the general SVR often fails to achieve good predictive performance due to its inability to assess feature contribution accurately. Feature weighting is a suitable solution to address this issue, applying correlation measurement methods to obtain reasonable weights for features based on their contributions to the output. In this paper, based on the idea of a Hilbert\u2013Schmidt independence criterion least absolute shrinkage and selection operator (HSIC LASSO) for selecting features with minimal redundancy and maximum relevance, we propose a novel feature-weighted SVR that considers the importance of features to the output and the redundancy between features. In this approach, the HSIC is utilized to effectively measure the correlation between features as well as that between features and the output. The feature weights are obtained by solving a LASSO regression problem. Compared to other feature weighting methods, our method takes much more comprehensive consideration of weight calculation, and the obtained weighted kernel function can lead to more precise predictions for unknown data. Comprehensive experiments on real datasets from the University of California Irvine (UCI) machine learning repository demonstrate the effectiveness of the proposed method.<\/jats:p>","DOI":"10.3390\/info15100639","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T07:28:35Z","timestamp":1729495715000},"page":"639","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Feature-Weighted Support Vector Regression Machine Based on Hilbert\u2013Schmidt Independence Criterion Least Absolute Shrinkage and Selection Operator"],"prefix":"10.3390","volume":"15","author":[{"given":"Xin","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2484-6719","authenticated-orcid":false,"given":"Tinghua","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China"}]},{"given":"Zhiyong","family":"Lai","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/72.788640","article-title":"An overview of statistical learning theory","volume":"10","author":"Vapnik","year":"1999","journal-title":"IEEE Trans. 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