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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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Main Authors: Moheb Pour, Majid Reza, Jantan, Adznan, Saripan, M. Iqbal
Format: Article
Language:English
Published: Universiti Putra Malaysia Press 2010
Online Access:http://psasir.upm.edu.my/id/eprint/40572/1/Grid%20Base%20Classifier%20in%20Comparison%20to%20Nonparametric%20Methods%20in%20Multiclass%20Classification.pdf
http://psasir.upm.edu.my/id/eprint/40572/
http://www.pertanika.upm.edu.my/Pertanika%20PAPERS/JST%20Vol.%2018%20%281%29%20Jan.%202010/18%20Pg%20139-154.pdf
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spelling my.upm.eprints.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
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
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.
format Article
author Moheb Pour, Majid Reza
Jantan, Adznan
Saripan, M. Iqbal
spellingShingle Moheb Pour, Majid Reza
Jantan, Adznan
Saripan, M. Iqbal
Grid base classifier in comparison to nonparametric methods in multiclass classification
author_facet Moheb Pour, Majid Reza
Jantan, Adznan
Saripan, M. Iqbal
author_sort Moheb Pour, Majid Reza
title 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_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_sort grid base classifier in comparison to nonparametric methods in multiclass classification
publisher Universiti Putra Malaysia Press
publishDate 2010
url http://psasir.upm.edu.my/id/eprint/40572/1/Grid%20Base%20Classifier%20in%20Comparison%20to%20Nonparametric%20Methods%20in%20Multiclass%20Classification.pdf
http://psasir.upm.edu.my/id/eprint/40572/
http://www.pertanika.upm.edu.my/Pertanika%20PAPERS/JST%20Vol.%2018%20%281%29%20Jan.%202010/18%20Pg%20139-154.pdf
_version_ 1643832754861768704
score 13.211869

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