Classification of Holy Quran Verses based on Imbalanced Learning

Bassam Arkok, Akram M. Zeki

Abstract


Imbalanced Learning (IL) is considered as a special case of text classification. It is applied in order to classify Imbalanced classes that are not equal in the number of samples. There are many researches on classified Quranic text which depends on different methods of classification. However, there is no study that classifies the Quranic topics based on Imbalanced Leaning. So, this paper aims to apply the concept of IL to assign corresponding topics for the Quranic verses according to their contents. In this paper, two Quranic datasets have been classified by using Imbalanced Learning consecutively; the first dataset is Unification of God “Tawheed” and Polytheism of God “Shirk” verses, the second dataset is Meccan, and Medinan chapters. Imbalanced Classification is applied here since these topics have imbalanced classes which cannot be classified correctly by traditional methods. The results showed that applying Imbalanced Classification produces better outcomes than the results that are executed without using Imbalanced Classification techniques.


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References


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