Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level
We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data...
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| Main Authors: | , , , |
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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/10.1155/2014/492387 |
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| _version_ | 1832558801237049344 |
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| author | Shehzad Khalid Sannia Arshad Sohail Jabbar Seungmin Rho |
| author_facet | Shehzad Khalid Sannia Arshad Sohail Jabbar Seungmin Rho |
| author_sort | Shehzad Khalid |
| collection | DOAJ |
| description | We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes. |
| format | Article |
| id | doaj-art-683b72198360407f9196323f77a28432 |
| 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-683b72198360407f9196323f77a284322025-02-03T01:31:30ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/492387492387Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class LevelShehzad Khalid0Sannia Arshad1Sohail Jabbar2Seungmin Rho3Department of Computer Engineering, Bahria University, Islamabad 44000, PakistanDepartment of Computer Engineering, Bahria University, Islamabad 44000, PakistanDepartment of Computer Science, COMSATS Institute of Information Technology, Sahiwal, PakistanDepartment of Multimedia, Sungkyul University, Anyang-si, Republic of KoreaWe have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes.http://dx.doi.org/10.1155/2014/492387 |
| spellingShingle | Shehzad Khalid Sannia Arshad Sohail Jabbar Seungmin Rho Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level The Scientific World Journal |
| title | Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level |
| title_full | Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level |
| title_fullStr | Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level |
| title_full_unstemmed | Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level |
| title_short | Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level |
| title_sort | robust framework to combine diverse classifiers assigning distributed confidence to individual classifiers at class level |
| url | http://dx.doi.org/10.1155/2014/492387 |
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