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Predicting Crude Oil Price Using Fuzzy Rough Set and Bio-Inspired Negative Selection Algorithm

Author

Listed:
  • Ayodele Lasisi

    (Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia)

  • Nasser Tairan

    (College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia)

  • Rozaida Ghazali

    (Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia)

  • Wali Khan Mashwani

    (Department of Mathematics, Kohat University of Science and Technology, Kohat, Pakistan)

  • Sultan Noman Qasem

    (Computer Science Department, College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia & Computer Science Department, Faculty of Applied Sciences, Taiz University, Taiz, Yemen)

  • Harish Kumar G R

    (College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia)

  • Anuja Arora

    (Jaypee Institute of Information Technology, Noida, India)

Abstract

The need to accurately predict and make right decisions regarding crude oil price motivates the proposition of an alternative algorithmic method based on real-valued negative selection with variable-sized detectors (V-Detectors), by incorporating with fuzzy-rough set feature selection (FRFS) for predicting the most appropriate choices. The objective of this study is enhancing the performance of V-Detectors using FRFS for prices of crude oil. Applying FRFS serves to prune the number of features by retaining the most informative and critical features. The V-Detectors then trains and tests the features. Different radius values are applied for V-Detectors. Experimental outcome in comparison with established algorithms such as support vector machine, naïve bayes, multi-layer perceptron, J48, non-nested generalized exemplars, IBk, fuzzy-roughNN, and vaguely quantified nearest neighbor demonstrates that FRFS-V-Detectors is proficient and valuable for insightful knowledge on crude oil price. Thus, it can assist in establishing oil price market policies on the international scale.

Suggested Citation

  • Ayodele Lasisi & Nasser Tairan & Rozaida Ghazali & Wali Khan Mashwani & Sultan Noman Qasem & Harish Kumar G R & Anuja Arora, 2019. "Predicting Crude Oil Price Using Fuzzy Rough Set and Bio-Inspired Negative Selection Algorithm," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 10(4), pages 25-37, October.
    • Handle: RePEc:igg:jsir00:v:10:y:2019:i:4:p:25-37
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