Abstract
Supervised data classification is one of the techniques used to extract nontrivial information from data. Classification is a widely used technique in various fields, including data mining, industry, medicine, science, and law. This paper considers a new algorithm for supervised data classification problems associated with the cluster analysis. The mathematical formulations for this algorithm are based on nonsmooth, nonconvex optimization. A new algorithm for solving this optimization problem is utilized. The new algorithm uses a derivative-free technique, with robustness and efficiency. To improve classification performance and efficiency in generating classification model, a new feature selection algorithm based on techniques of convex programming is suggested. Proposed methods are tested on real-world datasets. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithms.
Highlights
A New Method for Solving Supervised Data Classification ProblemsReceived 14 July 2014; Revised 19 October 2014; Accepted 6 November 2014; Published 27 November 2014
Supervised data classification is a widely used technique in various fields, including data mining, whose aim is to establish rules for the classification of some observations assuming that the classes of data are known
In this paper a new algorithm was proposed for solving classification problem where the algorithm includes the nonsmooth and nonconvex optimization problems
Summary
Received 14 July 2014; Revised 19 October 2014; Accepted 6 November 2014; Published 27 November 2014. Supervised data classification is one of the techniques used to extract nontrivial information from data. Classification is a widely used technique in various fields, including data mining, industry, medicine, science, and law. This paper considers a new algorithm for supervised data classification problems associated with the cluster analysis. The mathematical formulations for this algorithm are based on nonsmooth, nonconvex optimization. A new algorithm for solving this optimization problem is utilized. The new algorithm uses a derivative-free technique, with robustness and efficiency. To improve classification performance and efficiency in generating classification model, a new feature selection algorithm based on techniques of convex programming is suggested. Proposed methods are tested on real-world datasets. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithms
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