Abstract
The aim of this study is to develop the Bayesian LeastSquares Support Vector Machine (LS-SVM) classifiers for preoperative discrimination between benign and malignant ovarian tumors. We describe how to perform (hyper)parameter estimation, input variable selection for LS-SVMs within the evidence framework. The issue of computing the posterior class probability for risk minimization decision making is addressed. The performance of the LS-SVM models with linear and RBF kernels has been evaluated and compared with Bayesian multi-layer perceptrons (MLPs) and linear discriminant analysis.
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Lu, C., Van-Gestel, T., Suykens, J.A.K., Van-Huffel, S., Timmerman, D., Vergote, I. (2003). Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines. In: Dojat, M., Keravnou, E.T., Barahona, P. (eds) Artificial Intelligence in Medicine. AIME 2003. Lecture Notes in Computer Science(), vol 2780. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39907-0_31
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DOI: https://doi.org/10.1007/978-3-540-39907-0_31
Publisher Name: Springer, Berlin, Heidelberg
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