{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:30:58Z","timestamp":1781109058094,"version":"3.54.1"},"reference-count":44,"publisher":"IGI Global Scientific Publishing","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,1]]},"abstract":"<p>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, na\u00efve 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.<\/p>","DOI":"10.4018\/ijsir.2019100102","type":"journal-article","created":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T08:16:13Z","timestamp":1565165773000},"page":"25-37","source":"Crossref","is-referenced-by-count":10,"title":["Predicting Crude Oil Price Using Fuzzy Rough Set and Bio-Inspired Negative Selection Algorithm"],"prefix":"10.4018","volume":"10","author":[{"given":"Ayodele","family":"Lasisi","sequence":"first","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nasser","family":"Tairan","sequence":"additional","affiliation":[{"name":"College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rozaida","family":"Ghazali","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wali Khan","family":"Mashwani","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Kohat University of Science and Technology, Kohat, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sultan Noman","family":"Qasem","sequence":"additional","affiliation":[{"name":"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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Harish Kumar G R","sequence":"additional","affiliation":[{"name":"College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5215-1300","authenticated-orcid":true,"given":"Anuja","family":"Arora","sequence":"additional","affiliation":[{"name":"Jaypee Institute of Information Technology, Noida, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJSIR.2019100102-0","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2014.01.015"},{"key":"IJSIR.2019100102-1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2007.12.004"},{"key":"IJSIR.2019100102-2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2018.02.004"},{"key":"IJSIR.2019100102-3","article-title":"The VEC-NAR model for short-term forecasting of oil prices.","author":"F.Cheng","journal-title":"Energy Economics"},{"key":"IJSIR.2019100102-4","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-4585-18-7_23"},{"issue":"3","key":"IJSIR.2019100102-5","first-page":"302","article-title":"Neuro-genetic model for crude oil price prediction while considering the impact of uncertainties.","volume":"12","author":"H.Chiroma","year":"2016","journal-title":"International Journal of Oil, Gas and Coal Technology"},{"key":"IJSIR.2019100102-6","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2014.12.045"},{"key":"IJSIR.2019100102-7","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2010.08.024"},{"key":"IJSIR.2019100102-8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2010.08.006"},{"key":"IJSIR.2019100102-9","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-015-7975-9_14"},{"key":"IJSIR.2019100102-10","doi-asserted-by":"publisher","DOI":"10.5897\/JEIF2014.0629"},{"key":"IJSIR.2019100102-11","doi-asserted-by":"publisher","DOI":"10.1007\/s12647-013-0081-x"},{"key":"IJSIR.2019100102-12","doi-asserted-by":"publisher","DOI":"10.3934\/jimo.2014.10.777"},{"key":"IJSIR.2019100102-13","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-4666-7258-1.ch020"},{"key":"IJSIR.2019100102-14","doi-asserted-by":"publisher","DOI":"10.1016\/j.bjbas.2015.02.003"},{"key":"IJSIR.2019100102-15","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2015.05.001"},{"key":"IJSIR.2019100102-16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.11.041"},{"key":"IJSIR.2019100102-17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2014.02.008"},{"key":"IJSIR.2019100102-18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2008.09.026"},{"key":"IJSIR.2019100102-19","doi-asserted-by":"publisher","DOI":"10.3390\/su8040387"},{"key":"IJSIR.2019100102-20","doi-asserted-by":"publisher","DOI":"10.1037\/h0071325"},{"key":"IJSIR.2019100102-21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2011.07.018"},{"key":"IJSIR.2019100102-22","unstructured":"Jensen, R. 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