TY - GEN
T1 - Prediction of periventricular leukomalacia occurrence in neonates using a novel support vector machine classifier optimization method
AU - Bender, Dieter
AU - Jalali, Ali
AU - Licht, Daniel J.
AU - Nataraj, C.
N1 - Publisher Copyright:
© 2015 by ASME.
PY - 2015
Y1 - 2015
N2 - Prior work has documented that Support Vector Machine (SVM) classifiers can be powerful tools in predicting clinical outcomes of complex diseases such as Periventricular Leukomalacia (PVL). Our previous study showed that SVM performance can be improved significantly by optimizing the supervised training set used during the learning stage of the overall SVM algorithm. This study fully develops the initial idea using the reliable Leave-One-Out Cross-validation (LOOCV) technique. The work presented in this paper confirms previous results and improves the performance of the SVM even further. In addition, using the LOOCV technique, the computational time is decreased and the structure of the algorithm simplified, making this framework more feasible. Furthermore, we evaluate the performance of the resulting optimized SVM classifier on an unseen set of data. This demonstrates that the developed SVM algorithm outperforms normal SVM type classifiers without any loss of generalization.
AB - Prior work has documented that Support Vector Machine (SVM) classifiers can be powerful tools in predicting clinical outcomes of complex diseases such as Periventricular Leukomalacia (PVL). Our previous study showed that SVM performance can be improved significantly by optimizing the supervised training set used during the learning stage of the overall SVM algorithm. This study fully develops the initial idea using the reliable Leave-One-Out Cross-validation (LOOCV) technique. The work presented in this paper confirms previous results and improves the performance of the SVM even further. In addition, using the LOOCV technique, the computational time is decreased and the structure of the algorithm simplified, making this framework more feasible. Furthermore, we evaluate the performance of the resulting optimized SVM classifier on an unseen set of data. This demonstrates that the developed SVM algorithm outperforms normal SVM type classifiers without any loss of generalization.
UR - http://www.scopus.com/inward/record.url?scp=84973390040&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84973390040&partnerID=8YFLogxK
U2 - 10.1115/DSCC2015-9984
DO - 10.1115/DSCC2015-9984
M3 - Conference contribution
AN - SCOPUS:84973390040
T3 - ASME 2015 Dynamic Systems and Control Conference, DSCC 2015
BT - Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Control; Advances in Robotics; Advances in Wind Energy Systems; Aerospace Applications; Aerospace Power Optimization; Assistive Robotics; Automotive 2
PB - American Society of Mechanical Engineers
T2 - ASME 2015 Dynamic Systems and Control Conference, DSCC 2015
Y2 - 28 October 2015 through 30 October 2015
ER -