Grid base classifier in comparison to nonparametric methods in multiclass classification
In this paper, a new method known as Grid Base Classifier was proposed. This method carries the advantages of the two previous methods in order to improve the classification tasks. The problem with the current lazy algorithms is that they learn quickly, but classify very slowly. On the other hand, t...
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| Format: | Article |
| Language: | English |
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Universiti Putra Malaysia Press
2010
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| Online Access: | http://psasir.upm.edu.my/id/eprint/40572/ http://psasir.upm.edu.my/id/eprint/40572/1/Grid%20Base%20Classifier%20in%20Comparison%20to%20Nonparametric%20Methods%20in%20Multiclass%20Classification.pdf |
| _version_ | 1848849464974901248 |
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| author | Moheb Pour, Majid Reza Jantan, Adznan Saripan, M. Iqbal |
| author_facet | Moheb Pour, Majid Reza Jantan, Adznan Saripan, M. Iqbal |
| author_sort | Moheb Pour, Majid Reza |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In this paper, a new method known as Grid Base Classifier was proposed. This method carries the advantages of the two previous methods in order to improve the classification tasks. The problem with the current lazy algorithms is that they learn quickly, but classify very slowly. On the other hand, the eager algorithms classify quickly, but they learn very slowly. The two algorithms were compared, and the proposed algorithm was found to be able to both learn and classify quickly. The method was developed based on the grid structure which was done to create a powerful method for classification. In the current research, the new algorithm was tested and applied to the multiclass classification of two or more categories, which are important for handling problems related to practical classification. The new method was also compared with the Levenberg-Marquardt back-propagation neural network in the learning stage and the Condensed nearest neighbour in the generalization stage to examine the performance of the model. The results from the artificial and real-world data sets (from UCI Repository) showed that the new method could improve both the efficiency and accuracy of pattern classification. |
| first_indexed | 2025-11-15T09:50:49Z |
| format | Article |
| id | upm-40572 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:50:49Z |
| publishDate | 2010 |
| publisher | Universiti Putra Malaysia Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-405722018-10-26T02:08:48Z http://psasir.upm.edu.my/id/eprint/40572/ Grid base classifier in comparison to nonparametric methods in multiclass classification Moheb Pour, Majid Reza Jantan, Adznan Saripan, M. Iqbal In this paper, a new method known as Grid Base Classifier was proposed. This method carries the advantages of the two previous methods in order to improve the classification tasks. The problem with the current lazy algorithms is that they learn quickly, but classify very slowly. On the other hand, the eager algorithms classify quickly, but they learn very slowly. The two algorithms were compared, and the proposed algorithm was found to be able to both learn and classify quickly. The method was developed based on the grid structure which was done to create a powerful method for classification. In the current research, the new algorithm was tested and applied to the multiclass classification of two or more categories, which are important for handling problems related to practical classification. The new method was also compared with the Levenberg-Marquardt back-propagation neural network in the learning stage and the Condensed nearest neighbour in the generalization stage to examine the performance of the model. The results from the artificial and real-world data sets (from UCI Repository) showed that the new method could improve both the efficiency and accuracy of pattern classification. Universiti Putra Malaysia Press 2010-01 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/40572/1/Grid%20Base%20Classifier%20in%20Comparison%20to%20Nonparametric%20Methods%20in%20Multiclass%20Classification.pdf Moheb Pour, Majid Reza and Jantan, Adznan and Saripan, M. Iqbal (2010) Grid base classifier in comparison to nonparametric methods in multiclass classification. Pertanika Journal of Science & Technology, 18 (1). pp. 139-154. ISSN 0128-7680; ESSN: 2231-8526 http://www.pertanika.upm.edu.my/Pertanika%20PAPERS/JST%20Vol.%2018%20%281%29%20Jan.%202010/18%20Pg%20139-154.pdf |
| spellingShingle | Moheb Pour, Majid Reza Jantan, Adznan Saripan, M. Iqbal Grid base classifier in comparison to nonparametric methods in multiclass classification |
| title | Grid base classifier in comparison to nonparametric methods in multiclass classification |
| title_full | Grid base classifier in comparison to nonparametric methods in multiclass classification |
| title_fullStr | Grid base classifier in comparison to nonparametric methods in multiclass classification |
| title_full_unstemmed | Grid base classifier in comparison to nonparametric methods in multiclass classification |
| title_short | Grid base classifier in comparison to nonparametric methods in multiclass classification |
| title_sort | grid base classifier in comparison to nonparametric methods in multiclass classification |
| url | http://psasir.upm.edu.my/id/eprint/40572/ http://psasir.upm.edu.my/id/eprint/40572/ http://psasir.upm.edu.my/id/eprint/40572/1/Grid%20Base%20Classifier%20in%20Comparison%20to%20Nonparametric%20Methods%20in%20Multiclass%20Classification.pdf |