A New Method for Solving Supervised Data Classification Problems

• Main Authors: Parvaneh Shabanzadeh, Rubiyah Yusof
• Format: Article
• Language: English
• Published: Hindawi Limited 2014-01-01
• Series: Abstract and Applied Analysis
• Online Access: http://dx.doi.org/10.1155/2014/318478

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.