Abstract and Applied Analysis

A New Method for Solving Supervised Data Classification Problems

Parvaneh Shabanzadeh and Rubiyah Yusof

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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.

Article information

Source
Abstr. Appl. Anal., Volume 2014 (2014), Article ID 318478, 9 pages.

Dates
First available in Project Euclid: 27 February 2015

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1425049889

Digital Object Identifier
doi:10.1155/2014/318478

Mathematical Reviews number (MathSciNet)
MR3285157

Zentralblatt MATH identifier
1343.62038

Citation

Shabanzadeh, Parvaneh; Yusof, Rubiyah. A New Method for Solving Supervised Data Classification Problems. Abstr. Appl. Anal. 2014 (2014), Article ID 318478, 9 pages. doi:10.1155/2014/318478. https://projecteuclid.org/euclid.aaa/1425049889


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