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A-Test Method for Quantifying Structural Risk and Learning Capacity of Supervised Machine Learning Methods
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Department of Information Science and Media Studies, University of Bergen, Norway.
2022 (English)In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 289, p. 132-135Article in journal (Refereed) Published
Abstract [en]

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[1]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.

Place, publisher, year, edition, pages
Amsterdam, The Netherlands: IOS Press, 2022. Vol. 289, p. 132-135
Keywords [en]
A-Test method, structural risk, learning capacity, heart sounds
National Category
Engineering and Technology Computer Systems
Identifiers
URN: urn:nbn:se:liu:diva-182397DOI: 10.3233/shti210876PubMedID: 35062109OAI: oai:DiVA.org:liu-182397DiVA, id: diva2:1629456
Available from: 2022-01-17 Created: 2022-01-17 Last updated: 2022-02-08Bibliographically approved

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Gharehbaghi, ArashBabic, Ankica

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