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Grid base classifier in comparison to nonparametric methods in multiclass classification


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

Abstract / Synopsis

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.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
Publisher: Universiti Putra Malaysia Press
Keywords: Grid; Decision boundaries; Pattern recognition; Supervised; Nonparametric; Multiclass
Depositing User: Noor Syafini Zamani
Date Deposited: 20 Nov 2015 16:37
Last Modified: 26 Oct 2018 10:08
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