A New Method for Solving Supervised Data Classification Problems

Abstract EN

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.