Bayesian Least Squares Support Vector Machines for Classification of Ovarian Tumors

The aim of this study is to develop the Bayesian Least Squares Support Vector Machine (LS-SVM) classifiers, for preoperatively predicting the malignancy of ovarian tumors. We describe how to perform parameter estimation, input variable selection for LS-SVM within the evidence framework. The issue of computing the posterior class probability for risk minimization decision making is addressed. The relation between the LS-SVM model and kernel principal component analysis is also indicated and used for interpretation of the LS-SVM classifiers.

[1]  J Vandewalle,et al.  Artificial neural network models for the preoperative discrimination between malignant and benign adnexal masses , 1999, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.

[2]  T. Bourne,et al.  Terms, definitions and measurements to describe the sonographic features of adnexal tumors: a consensus opinion from the International Ovarian Tumor Analysis (IOTA) group , 2000, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.

[3]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[4]  H. Jeffreys,et al.  The Theory of Probability , 1896 .

[5]  D. Timmerman,et al.  Using artificial neural networks to predict malignancy of ovarian tumors , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[7]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[8]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[9]  David J. C. MacKay,et al.  The Evidence Framework Applied to Classification Networks , 1992, Neural Computation.

[10]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[11]  Johan A. K. Suykens,et al.  Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis , 2002, Neural Computation.