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dc.contributor.authorGharehbaghi, Arash
dc.contributor.authorBabic, Ankica
dc.description.abstractThis 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.publisherIOS Pressen_US
dc.rightsNavngivelse-Ikkekommersiell 4.0 Internasjonal*
dc.titleA-Test Method for Quantifying Structural Risk and Learning Capacity of Supervised Machine Learning Methodsen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.rights.holderCopyright 2022 The authors and IOS Press.en_US
dc.source.journalStudies in Health Technology and Informaticsen_US
dc.identifier.citationStudies in Health Technology and Informatics. 2022, 289, 132-135.en_US

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Navngivelse-Ikkekommersiell 4.0 Internasjonal
Med mindre annet er angitt, så er denne innførselen lisensiert som Navngivelse-Ikkekommersiell 4.0 Internasjonal