A Biomedical Decision Support System Using LS-SVM Classifier with an Efficient and New Parameter Regularization Procedure for Diagnosis of Heart Valve Diseases
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Date
2012
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
SPRINGER
Access Rights
info:eu-repo/semantics/closedAccess
Abstract
Classification success of Support Vector Machine (SVM) depends on the characteristic of given data set and some training parameters (C and sigma). In literature, a few studies have been presented for regularization of these parameters which affects classification performance directly. This study proposes a new approach based on Renyi's entropy and Logistic regression methods for parameter regularization. Our regularization procedure runs at two steps. In the first step, optimal value of kernel parameter interval is found via Renyi's entropy method and optimal C value is found via logistic regression using exponential function in the next step. In addition to, this new decision support system is applied to biomedical research area via an application related to Doppler Heart Sounds (DHS). Experimental results show the efficiency of developed regularization procedure.
Description
Keywords
Doppler heart sounds, Feature extraction, Support vector machines, Decision support systems, Parameter regularization
Journal or Series
JOURNAL OF MEDICAL SYSTEMS
WoS Q Value
Q2
Scopus Q Value
Q1
Volume
36
Issue
2
