TY - GEN
T1 - Optimal parameter selection of an SVM model
T2 - 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
AU - Jiang, Peng
AU - Missoum, Samy
AU - Hu, Chengcheng
AU - Chen, Zhao
N1 - Funding Information:
The authors are thankful to Mykola M. Khrystenko for helping in the creation of the fully parameterized finite element model of a femur. The support of NIH through grant NIAMS 1R21AR060811-01 is gratefully acknowledged.
PY - 2013
Y1 - 2013
N2 - This article presents a study of three (cross) validation metrics used for the selection of the optimal parameters of a support vector machine (SVM) classifier. The study focuses on problems for which the data is non-separable and unbalanced as is often the case for experimental and clinical data. The three metrics selected in this work are the area under the ROC curve, accuracy, and balanced accuracy. As a test example, the study investigates the optimal parameters for an SVM classification model for hip fracture. The hip fracture data is obtained from a finite element model that is fully parameterized. Because the data is computational, fully separable sets of data (fracture and safe) can be obtained. By projection onto a lower dimensional sub-space, the data becomes non-separable and is used to construct the SVM. The knowledge of the separable case provides a comparison metric (the weighted likelihood) that would be unknown if only clinical data is used. The performance of the various metrics are compared for several levels of separability, unbalance and size of training samples. A probabilistic SVM is used to compute the probability of fracture.
AB - This article presents a study of three (cross) validation metrics used for the selection of the optimal parameters of a support vector machine (SVM) classifier. The study focuses on problems for which the data is non-separable and unbalanced as is often the case for experimental and clinical data. The three metrics selected in this work are the area under the ROC curve, accuracy, and balanced accuracy. As a test example, the study investigates the optimal parameters for an SVM classification model for hip fracture. The hip fracture data is obtained from a finite element model that is fully parameterized. Because the data is computational, fully separable sets of data (fracture and safe) can be obtained. By projection onto a lower dimensional sub-space, the data becomes non-separable and is used to construct the SVM. The knowledge of the separable case provides a comparison metric (the weighted likelihood) that would be unknown if only clinical data is used. The performance of the various metrics are compared for several levels of separability, unbalance and size of training samples. A probabilistic SVM is used to compute the probability of fracture.
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U2 - 10.2514/6.2013-1935
DO - 10.2514/6.2013-1935
M3 - Conference contribution
AN - SCOPUS:84880770001
SN - 9781624102233
T3 - 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
BT - 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
Y2 - 8 April 2013 through 11 April 2013
ER -