A Hybrid Feature Selection Technique for Classification of Group-based Holy Quran Verses

  • Authors

    • A. Adeleke
    • N. Samsudin
    2018-12-09
    https://doi.org/10.14419/ijet.v7i4.31.23372
  • Feature Selection Techniques, Holy Quran, Text Classification Algorithms, AUC, ROC Curve

  • Abstract

    Text classification problem is primarily applied in document labeling. However, the major setbacks with the existing feature selection techniques are high computational runtime associated with wrapper-based FS techniques and low classification accuracy performance associated with filter-based FS techniques. In this paper, a hybrid feature selection technique is proposed. The proposed hybrid technique is a combination of filter-based information gain (IG) and wrapper-based CFS algorithms. The specific purpose for this combination is to achieve both high classification accuracy performance (associated with wrapper) at lower computational runtime (associated with filter). The proposed IG-CFS technique is then applied to label Quranic verses of al-Baqara and al-Anaam from two major references, the English translation and commentary (tafsir). StringToWordVector with weighted TF-IDF method were used for preprocessing the textual data while four classifiers: naïve bayes, libSVM, k-NN, and decision trees (J48) were experimented. The overall highest classification accuracy of 94.5% was achieved at 3.89secs runtime with the proposed IG-CFS technique.

     

     

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  • How to Cite

    Adeleke, A., & Samsudin, N. (2018). A Hybrid Feature Selection Technique for Classification of Group-based Holy Quran Verses. International Journal of Engineering & Technology, 7(4.31), 228-233. https://doi.org/10.14419/ijet.v7i4.31.23372

    Received date: 2018-12-07

    Accepted date: 2018-12-07

    Published date: 2018-12-09