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The Scientific World Journal
Volume 2014 (2014), Article ID 313164, 9 pages
http://dx.doi.org/10.1155/2014/313164
Research Article

Global Optimization Ensemble Model for Classification Methods

Department of Computer Engineering, College of Electrical & Mechanical Engineering (E&ME), National University of Sciences and Technology (NUST), H-12, Islamabad 46000, Pakistan

Received 24 February 2014; Accepted 19 March 2014; Published 27 April 2014

Academic Editors: N. Barsoum, V. N. Dieu, P. Vasant, and G.-W. Weber

Copyright © 2014 Hina Anwar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity.