Description:
In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
Description:
In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
Description:
We discuss the applicability of the cutting angle method to global minimization of marginal functions. The search of equilibrium prices in the exchange model can be reduced to the global minimization of certain functions, which include marginal functions. This problem has been approximately solved by the cutting angle method. Results of numerical experiments are presented and discussed.
Description:
We reduce the supervised classification to solving a nonsmooth optimization problem. The proposed method allows one to solve classification problems for databases with arbitrary number of classes. Numerical experiments have been carried out with databases of small and medium size. We present their results and provide comparison of these results with ones obtained by other algorithms of classification based on the optimization techniques. Results of numerical experiments show effectiveness of the proposed algorithms.
Description:
We reduce the supervised classification to solving a nonsmooth optimization problem. The proposed method allows one to solve classification problems for databases with arbitrary number of classes. Numerical experiments have been carried out with databases of small and medium size. We present their results and provide comparison of these results with ones obtained by other algorithms of classification based on the optimization techniques. Results of numerical experiments show effectiveness of the proposed algorithms.
Description:
"The main objective of this thesis is to develop and study new techniques for solving global optimization problems and to apply them to solving data classification problems."
Description:
"The main objective of this thesis is to develop and study new techniques for solving global optimization problems and to apply them to solving data classification problems."
Description:
Global optimization-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with data from various databases. First, we discuss feature selection, the problem of determining the most informative features for classification in the databases under consideration. Then, we apply a technique based on convex and global optimization for classification in these databases. The third application of this technique is a method that calculates centers of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves high accuracy with these databases. Better classifiers will lead to improved assistance in making medical diagnostic and prognostic decisions.