Global Optimization Ensemble Model for Classification Methods
Joint Authors
Anwar, Hina
Qamar, Usman
Muzaffar Qureshi, Abdul Wahab
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-27
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Anwar, Hina& Qamar, Usman& Muzaffar Qureshi, Abdul Wahab. 2014. Global Optimization Ensemble Model for Classification Methods. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1049187
Modern Language Association (MLA)
Anwar, Hina…[et al.]. Global Optimization Ensemble Model for Classification Methods. The Scientific World Journal No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1049187
American Medical Association (AMA)
Anwar, Hina& Qamar, Usman& Muzaffar Qureshi, Abdul Wahab. Global Optimization Ensemble Model for Classification Methods. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1049187
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049187