A Hybrid Feature Selection Technique for Classification of Group-based Holy Quran Verses - Adeleke, A.; Samsudin, N. (International Journal of Engineering and Technology, 2018)
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: naÃ¯ve 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.Â Â