Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines

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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