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Classification of Quranic topics using ensemble learning

Bassam, Arkok and Khedher, Akram M Z M (2021) Classification of Quranic topics using ensemble learning. In: 8th International Conference on Computer and Communication Engineering, ICCCE 2021, 22-23 June 2021, Kuala Lumpur.

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Abstract—the real datasets in the world usually are imbalanced; the number of samples for their classes is not equal. Classifying these datasets makes the classifiers pay attention to the class with more samples than the classes with fewer samples. The Qur’anic dataset can be considered an imbalanced dataset because verses of the Qur’anic topics are not equal. Many studies have been performed to classify Qur’anic text using different classifiers. However, few studies classified the Qur’anic verses based on Imbalanced Learning (IL). So, this work aims to classify the Qur’anic text using Ensemble methods, Boosting and Bagging. The base classifiers of these methods were LibSVM, Naïve Bayes, KNN, and J48. Three techniques are conducted in this paper based on the standard classifiers. The three techniques are: implementing the base classifiers alone, implementing these classifiers with the Boosting method, and implementing the classifiers with the Bagging method. The results showed that the Quranic classification performance was improved when the ensemble methods were applied for the imbalanced Qur’anic verses in the standard classifiers.

Item Type: Conference or Workshop Item (Plenary Papers)
Uncontrolled Keywords: Classification, Quran, imbalanced, topics, ensemble methods
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Information and Communication Technology > Department of Information System
Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User: Akram M Zeki
Date Deposited: 04 Mar 2022 08:28
Last Modified: 04 Mar 2022 08:28
URI: http://irep.iium.edu.my/id/eprint/97022

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