Abstract
Most existing feature selection approach is limited to determine features from a single source of data. In this paper, a feature selection approach is proposed to consider multiple sources of textual data. The proposed GBFS approach is then applied to label Quranic verses based on two major references, the English translation and tafsir (Commentary). The verses were selected from two chapters, Surah Al-Baqarah and Surah Al-Anaam. The verses are classified into three categories: Faith, Worship, and Etiquette. The textual data from the translation and commentary were preprocessed using StringToWord Vector with weighted TF-IDF. Feature selection algorithms: information gain, chi square, Pearson correlation coefficient, relief, and correlation-based were experimented on four classifiers: naïve Bayes, libSVM, k-NN, and decision trees (J48). The proposed group-based feature selection approach has shown promising results in terms of Accuracy and Area under Receiver Operating Characteristics (ROC) curve (AUC) by achieving Accuracy of 94.5% and AUC of 0.944.
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Acknowledgements
This study was supported in part by a grant from the Ministry of Education of Malaysia, Research Acculturation Grant Scheme (RAGS) Vot R045, a grant from Universiti Tun Hussein Onn Malaysia Vot U611, and in part by a grant from Research Gates IT Solution Sdn. Bhd.
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Adeleke, A.O., Samsudin, N.A., Mustapha, A., Nawi, N.M. (2018). A Group-Based Feature Selection Approach to Improve Classification of Holy Quran Verses. In: Ghazali, R., Deris, M., Nawi, N., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2018. Advances in Intelligent Systems and Computing, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-319-72550-5_28
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