{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T01:55:18Z","timestamp":1777168518939,"version":"3.51.4"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643682501","type":"print"},{"value":"9781643682518","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T00:00:00Z","timestamp":1642118400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,14]]},"abstract":"<jats:p>This paper presents an original method for studying the performance of the supervised Machine Learning (ML) methods, the A-Test method. The method offers the possibility of investigating the structural risk as well as the learning capacity of ML methods in a quantitating manner. A-Test provides a powerful validation method for the learning methods with small or medium size of the learning data, where overfitting is regarded as a common problem of learning. Such a condition can occur in many applications of bioinformatics and biomedical engineering in which access to a large dataset is a challengeable task. Performance of the A-Test method is explored by validation of two ML methods, using real datasets of heart sound signals. The datasets comprise of children cases with a normal heart condition as well as 4 pathological cases: aortic stenosis, ventricular septal defect, mitral regurgitation, and pulmonary stenosis. It is observed that the A-Test method provides further comprehensive and more realistic information about the performance of the classification methods as compared to the existing alternatives, the K-fold validation and repeated random sub-sampling.<\/jats:p>","DOI":"10.3233\/shti210876","type":"book-chapter","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T15:49:56Z","timestamp":1642434596000},"source":"Crossref","is-referenced-by-count":2,"title":["A-Test Method for Quantifying Structural Risk and Learning Capacity of Supervised Machine Learning Methods"],"prefix":"10.3233","author":[{"given":"Arash","family":"Gharehbaghi","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Link\u00f6ping University, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ankica","family":"Babic","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Link\u00f6ping University, Sweden"},{"name":"Department of Information Science and Media Studies, University of Bergen, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Informatics and Technology in Clinical Care and Public Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI210876","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T15:49:59Z","timestamp":1642434599000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI210876"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,14]]},"ISBN":["9781643682501","9781643682518"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti210876","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,14]]}}}