Case-based support vector optimization for medical-imaging imbalanced datasets Conference Paper


Authors: Illan, I. A.; Ramirez, J.; Gorriz, J. M.; Pinker, K.; Meyer-Baese, A.
Title: Case-based support vector optimization for medical-imaging imbalanced datasets
Conference Title: International Joint Conference SOCO '18 - CISIS '18 - ICEUTE '18
Abstract: Imbalanced datasets constitute a challenge in medical-image processing and machine learning in general. When the available training data is highly imbalanced, the risk for a classifier to find the trivial solution increases dramatically. To control the risk, an estimate on the prior class probabilities is usually required. In some medical datasets, such as breast cancer imaging techniques, estimates on the priors are intractable. Here we propose a solution to the imbalanced support vector classification problem when prior estimations are absent based on a case-dependent transformation on the decision function. © 2019, Springer International Publishing AG, part of Springer Nature.
Keywords: magnetic resonance imaging; medical imaging; artificial intelligence; image processing; dce-mri; support vector machines; risk perception; classification (of information); learning systems; multiobjective optimization; medical image processing; breast cancer imaging; imbalanced datasets; soft computing; class probabilities; decision functions; imbalanced data-sets; medical data sets; support vector classification; trivial solutions
Journal Title Advances in Intelligent Systems and Computing
Volume: 771
Conference Dates: 2018 Jun 6-8
Conference Location: San Sebastian, Spain
ISBN: 2194-5357
Publisher: Springer International Publishing Ag  
Location: Cham, Switzerland
Date Published: 2019-01-01
Start Page: 221
End Page: 229
Language: English
DOI: 10.1007/978-3-319-94120-2_21
PROVIDER: scopus
DOI/URL:
Notes: ISBN: 978-3-319-94120-2 -- Conference Paper -- Export Date: 2 July 2018 -- Source: Scopus
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