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An Arabic text categorization approach using term weighting and multiple reducts.

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Title: An Arabic text categorization approach using term weighting and multiple reducts.
Authors: Al-Radaideh, Qasem A.1 (AUTHOR) qasemr@yu.edu.jo, Al-Abrat, Mohammed A.1 (AUTHOR) moh_alabrat@yahoo.com
Source: Soft Computing - A Fusion of Foundations, Methodologies & Applications. Jul2019, Vol. 23 Issue 14, p5849-5863. 15p.
Subjects: Rough sets, Number theory, Classification algorithms, Extraction (Chemistry)
Abstract: Text categorization is the process of assigning a predefined category label to an unlabeled document based on its content. One of the challenges of automatic text categorization is the high dimensionality of data that may affect the performance of the categorization model. This paper proposed an approach for the categorization of Arabic text based on term weighting and the reduct concept of the rough set theory to reduce the number of terms used to generate the classification rules that form the classifier. The paper proposed a multiple minimal reduct extraction algorithm by improving the Quick reduct algorithm. The multiple reducts are used to generate the set of classification rules which represent the rough set classifier. To evaluate the proposed approach, an Arabic corpus of 2700 documents nine categories is used. In the experiment, we compared the results of the proposed approach when using multiple and single minimal reducts. The results showed that the proposed approach had achieved an accuracy of 94% when using multiple reducts, which outperformed the single reduct method which achieved an accuracy of 86%. The results of the experiments also showed that the proposed approach outperforms both the K-NN and J48 algorithms regarding classification accuracy using the dataset on hand. [ABSTRACT FROM AUTHOR]
Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Al-Radaideh%2C+Qasem+A%2E%22">Al-Radaideh, Qasem A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> qasemr@yu.edu.jo</i><br /><searchLink fieldCode="AR" term="%22Al-Abrat%2C+Mohammed+A%2E%22">Al-Abrat, Mohammed A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> moh_alabrat@yahoo.com</i>
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  Data: <searchLink fieldCode="DE" term="%22Rough+sets%22">Rough sets</searchLink><br /><searchLink fieldCode="DE" term="%22Number+theory%22">Number theory</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Extraction+%28Chemistry%29%22">Extraction (Chemistry)</searchLink>
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  Data: Text categorization is the process of assigning a predefined category label to an unlabeled document based on its content. One of the challenges of automatic text categorization is the high dimensionality of data that may affect the performance of the categorization model. This paper proposed an approach for the categorization of Arabic text based on term weighting and the reduct concept of the rough set theory to reduce the number of terms used to generate the classification rules that form the classifier. The paper proposed a multiple minimal reduct extraction algorithm by improving the Quick reduct algorithm. The multiple reducts are used to generate the set of classification rules which represent the rough set classifier. To evaluate the proposed approach, an Arabic corpus of 2700 documents nine categories is used. In the experiment, we compared the results of the proposed approach when using multiple and single minimal reducts. The results showed that the proposed approach had achieved an accuracy of 94% when using multiple reducts, which outperformed the single reduct method which achieved an accuracy of 86%. The results of the experiments also showed that the proposed approach outperforms both the K-NN and J48 algorithms regarding classification accuracy using the dataset on hand. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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