dc.contributor.author | Gharehbaghi, Arash | |
dc.contributor.author | Babic, Ankica | |
dc.date.accessioned | 2022-02-07T09:11:36Z | |
dc.date.available | 2022-02-07T09:11:36Z | |
dc.date.created | 2022-01-17T21:39:28Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 0926-9630 | |
dc.identifier.uri | https://hdl.handle.net/11250/2977400 | |
dc.description.abstract | 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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IOS Press | en_US |
dc.rights | Navngivelse-Ikkekommersiell 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/deed.no | * |
dc.title | A-Test Method for Quantifying Structural Risk and Learning Capacity of Supervised Machine Learning Methods | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | Copyright 2022 The authors and IOS Press. | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.doi | 10.3233/SHTI210876 | |
dc.identifier.cristin | 1983054 | |
dc.source.journal | Studies in Health Technology and Informatics | en_US |
dc.source.pagenumber | 132-135 | en_US |
dc.identifier.citation | Studies in Health Technology and Informatics. 2022, 289, 132-135. | en_US |
dc.source.volume | 289 | en_US |