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

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.