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A-Test Method for Q...
A-Test Method for Quantifying Structural Risk and Learning Capacity of Supervised Machine Learning Methods
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- Gharehbaghi, Arash, 1972- (author)
- Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten
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- Babic, Ankica, 1960- (author)
- Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten,Department of Information Science and Media Studies, University of Bergen, Norway
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(creator_code:org_t)
- Amsterdam, The Netherlands : IOS Press, 2022
- 2022
- English.
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In: Studies in Health Technology and Informatics. - Amsterdam, The Netherlands : IOS Press. - 0926-9630 .- 1879-8365. ; 289, s. 132-135
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Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Keyword
- A-Test method
- structural risk
- learning capacity
- heart sounds
Publication and Content Type
- ref (subject category)
- art (subject category)
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