Global Optimization Ensemble Model for Classification Methods
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...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/313164 |
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| author | Hina Anwar Usman Qamar Abdul Wahab Muzaffar Qureshi |
| author_facet | Hina Anwar Usman Qamar Abdul Wahab Muzaffar Qureshi |
| author_sort | Hina Anwar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7c39099c6a0c4369b52dcbde426e1463 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-7c39099c6a0c4369b52dcbde426e14632025-02-03T01:26:09ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/313164313164Global Optimization Ensemble Model for Classification MethodsHina Anwar0Usman Qamar1Abdul Wahab Muzaffar Qureshi2Department of Computer Engineering, College of Electrical & Mechanical Engineering (E&ME), National University of Sciences and Technology (NUST), H-12, Islamabad 46000, PakistanDepartment of Computer Engineering, College of Electrical & Mechanical Engineering (E&ME), National University of Sciences and Technology (NUST), H-12, Islamabad 46000, PakistanDepartment of Computer Engineering, College of Electrical & Mechanical Engineering (E&ME), National University of Sciences and Technology (NUST), H-12, Islamabad 46000, PakistanSupervised 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.http://dx.doi.org/10.1155/2014/313164 |
| spellingShingle | Hina Anwar Usman Qamar Abdul Wahab Muzaffar Qureshi Global Optimization Ensemble Model for Classification Methods The Scientific World Journal |
| title | Global Optimization Ensemble Model for Classification Methods |
| title_full | Global Optimization Ensemble Model for Classification Methods |
| title_fullStr | Global Optimization Ensemble Model for Classification Methods |
| title_full_unstemmed | Global Optimization Ensemble Model for Classification Methods |
| title_short | Global Optimization Ensemble Model for Classification Methods |
| title_sort | global optimization ensemble model for classification methods |
| url | http://dx.doi.org/10.1155/2014/313164 |
| work_keys_str_mv | AT hinaanwar globaloptimizationensemblemodelforclassificationmethods AT usmanqamar globaloptimizationensemblemodelforclassificationmethods AT abdulwahabmuzaffarqureshi globaloptimizationensemblemodelforclassificationmethods |