Abstract
This paper implements the real-valued negative selection with variable-sized detectors (V-Detectors) for projecting the right decision with respect to crude oil price. The Brent crude oil data is retrieved from US department of energy. Using varying radius values of the V-Detector, comparison in terms of detection rate and false alarm rate, with support vector machine, naïve bayes, multi-layer perceptron, J48, non-nested generalized exemplars, IBk, fuzzy-roughNN, and vaguely quantified nearest neighbor demonstrated that V-Detector is efficient and computationally effective. The experimental outcome can initiate international crude oil market policy making as the V-Detector is able to reach highest detection and lowest false alarm rates.
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Acknowledgements
This work is supported by the Office for Research, Innovation, Commercialization, and Consultancy Management (ORICC), Universiti Tun Hussein Onn Malaysia (UTHM), and Ministry of Higher Education (MOHE) Malaysia under the Fundamental Research Grant Scheme (FRGS) Vote No. 1235.
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Lasisi, A., Ghazali, R., Herawan, T., Chiroma, H. (2015). Orchestrating Real-Valued Negative Selection Algorithm with Computational Efficiency for Crude Oil Price. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_42
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DOI: https://doi.org/10.1007/978-3-319-22053-6_42
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