Classification of Quranic Topics Using Ensemble Learning

Arkok, Bassam; Zeki, Ahmed M.

doi:10.1109/iccce50029.2021.9467178

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…Arkok et al proposed an improved classification method for imbalanced Quranic datasets using ensemble methods, specifically boosting and bagging [13]. The authors have compared the performance of different classifiers, including LibSVM, Naïve Bayes, KNN, and J48, with and without ensemble methods.…”

Section: Alharbi Et Al Proposed a Deep Learning-based Model Formentioning

confidence: 99%

Analytics of ensemble learning-based methods for Arabic text classification

Sabri,

Kissi,

Bahassine

et al. 2023

2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA)

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In recent years, the volume of unstructured documents has grown exponentially, necessitating a profound understanding of Machine Learning (ML) methods to accurately classify documents in various domains, including business, media, sports, and more. This study proposes the utilization of ensemble learning methods for the automated classification of new documents. Specifically, ML algorithms and their bagging, boosting, stacking, and voting methods are employed. A comparative analysis of these algorithms is conducted using three benchmark Arabic datasets. The experimental results demonstrate that ensemble learning methods outperform individual learners in terms of accuracy, precision, recall, and F-measure. Among the ensemble learning classifiers, the stacking and voting methods exhibit the most promising performance, achieving the highest accuracy of 95.20% with the ANTCorpus dataset. Additionally, voting has led to improved accuracy of 93.24% for the Khaleej-2004 dataset and 92.15% for the Watan-2004 dataset.

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Section: Alharbi Et Al Proposed a Deep Learning-based Model Formentioning

confidence: 99%

Analytics of ensemble learning-based methods for Arabic text classification

Sabri,

Kissi,

Bahassine

et al. 2023

2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA)

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“…The GOS algorithm was evaluated on 8 binary Quranic datasets with different imbalance ratios. The Quranic datasets were applied by other studies [9][10][11] that were extracted from the Quranic Index collected by Dr Abu Akhir [12]. Table I presents these datasets with their imbalanced ratios and the number of verses in the majority and minority classes.…”

Section: A Datasetsmentioning

confidence: 99%

GOS: A Genetic OverSampling Algorithm for Classification of Quranic Verses

Arkok

Zeki

2022

2022 13th International Conference on Information and Communication Systems (ICICS)

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Imbalanced classes problem is a problem in many datasets in real applications, where one class "minority class" contain few numbers of samples and the other "majority class" contain many numbers of samples. It is difficult to build a training model to classify the imbalanced classes correctly due to tending the accuracy of classification to the majority class. In this paper, a new technique is called "GOS: a Genetic OverSampling algorithm", is proposed using a genetic algorithm. A genetic algorithm is applied to oversample the imbalanced datasets and to improve the performance of imbalanced classification. This improvement is achieved due to adjusting the locations of samples in the minority class in the optimal places. According to the experimental results obtained, the GOS algorithm outperformed other techniques used widely in the imbalanced classification field.

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“…Tabel 6. Analisis Metode Representasi Teks dalam Arabic Natural Language Processing Metode Untuk Representasi Teks Peneliti Jumlah TF-IDF [4], [9], [48], [30], [23], [24], [29], [53], [54], [7], [38], [55], [32], [56], [25], [31], [59], [1], [51], [60] 20 Word2Vec [34], [24], [49], [7], [57], [39], [41], [43], [45], [46] 10 AraVec [34], [35], [37], [40], [41], [42] 6 FastText [34], [47], [35], [57], [41], [42], [50], [46] 8 mBERT [27], [22], [41], [46] 4 AraBERT [36],…”

Section: Tahap Pembuatan Rencana Awalmentioning

confidence: 99%

Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

Ariyanto

fatichah²,

Arifin³

2021

RESTI

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Hadith is the second source of reference for Islamic law after the Qur'an, which explains the sentences in the Qur'an which are still global by referring to the provisions of the Prophet Muhammad SAW. Classification of text documents can also be used to overcome the problem of interrelation between the Qur'an and hadith. The problem of interrelation between books of hadith needs to be done because some hadiths in certain hadith books have the same meaning as other hadith books. This study aims to analyze the development of text representation and classification methods suitable to overcome similarity meaning problems in detecting interrelationships between hadith books. The research method used is Systematic Literature Review (SLR) sourced from Google Scholar, Science Direct, and IEEE. There are 42 pieces of literature that have been studied successfully. The results showed that contextual embedding as the newest text representation method considered word context and sentence meaning better than static embedding. As a classification method, the ensemble method has better performance in classifying text documents than using only a single classifier model. Thus, future research can consider using a combination of contextual embedding and ensemble methods to detect interrelationships between books of hadith.

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Classification of Quranic Topics Using Ensemble Learning

Cited by 4 publications

References 28 publications

Analytics of ensemble learning-based methods for Arabic text classification

Analytics of ensemble learning-based methods for Arabic text classification

GOS: A Genetic OverSampling Algorithm for Classification of Quranic Verses

Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

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