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

Ayodele Lasisi, Nasser Tairan, Rozaida Ghazali, Wali Khan Mashwani, Sultan Noman Qasem, Harish Kumar G R and Anuja Arora
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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

International Journal of Swarm Intelligence Research (IJSIR), 2019, vol. 10, issue 4, 25-37

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.

Date: 2019
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