Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12358/25073
Title | Design and evaluation of a parallel classifier for large-scale Arabic text |
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Abstract |
Text classification has become one of the most important techniques in text mining. A number of machine learning algorithms have been introduced to deal with automatic text classification. One of the common classification algorithms is the k-NN algorithm which is known to be one of the best classifiers applied for different languages including Arabic language. However, the k-NN algorithm is of low efficiency because it requires a large amount of computational power. Such a drawback makes it unsuitable to handle a large volume of text documents with high dimensionality and in particular in the Arabic language. This paper introduces a high performance parallel classifier for large-scale Arabic text that achieves the enhanced level of speedup, scalability, and accuracy. The parallel classifier is based on the sequential k-NN algorithm. The classifier has been tested using the OSAC corpus. The performance of the parallel classifier has been studied on a multicomputer cluster. The results indicate that the parallel classifier has very good speedup and scalability and is capable of handling large documents collections with higher classification results. |
Authors | |
Type | Journal Article |
Date | 2013 |
Published in | International Journal of Computer Applications |
Series | Volume: 75, Number: 3 |
Publisher | Foundation of Computer Science |
Citation | |
Item link | Item Link |
License | ![]() |
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Files in this item | ||
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Baraka, Rebhi S._9.pdf | 588.7Kb |