Ant system-based feature set partitioning algorithm for K-NN and LDA ensembles construction
Combination of several classifiers has been very useful in improving the prediction accuracy and in most situations multiple classifiers perform better than single classifier.However not all combining approaches are successful at producing multiple classifiers with good classification accuracy becau...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/15575/1/PID222.pdf http://repo.uum.edu.my/15575/ http://www.icoci.cms.net.my/proceedings/2015/index.html |
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| Institution: | Universiti Utara Malaysia |
| Language: | English |
| Summary: | Combination of several classifiers has been very useful in improving the prediction accuracy and in most situations multiple classifiers perform better than single classifier.However not all combining approaches are successful at producing multiple classifiers with good classification accuracy because there is no standard resolution in constructing diverse and accurate classifier ensemble.This paper proposes ant system-based feature set partitioning algorithm in constructing k-nearest neighbor (k-NN) and linear discriminant analysis (LDA) ensembles. Experiments were performed on several University California, Irvine datasets to test the performance of the proposed algorithm.Experimental results showed that the proposed algorithm has successfully constructed better classifier ensemble for k-NN and LDA. |
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