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Syst."],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Probabilistic predictions for regression problems are more popular than point predictions and interval predictions, since they contain more information for test labels. Conformal predictive system is a recently proposed non-parametric method to do reliable probabilistic predictions, which is computationally inefficient due to its learning process. To build faster conformal predictive system and make full use of training data, this paper proposes the predictive system based on locally weighted jackknife prediction approach. The theoretical property of our proposed method is proved with some regularity assumptions in the asymptotic setting, which extends our earlier theoretical researches from interval predictions to probabilistic predictions. In the experimental section, our method is implemented based on our theoretical analysis and its comparison with other predictive systems is conducted using 20 public data sets. The continuous ranked probability scores of the predictive distributions and the performance of the derived prediction intervals are compared. The better performance of our proposed method is confirmed with Wilcoxon tests. The experimental results demonstrate that the predictive system we proposed is not only empirically valid, but also provides more information than the other comparison predictive systems.<\/jats:p>","DOI":"10.1007\/s40747-023-01044-0","type":"journal-article","created":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T10:25:07Z","timestamp":1680690307000},"page":"5761-5778","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Probabilistic prediction with locally weighted jackknife predictive system"],"prefix":"10.1007","volume":"9","author":[{"given":"Di","family":"Wang","sequence":"first","affiliation":[]},{"given":"Ping","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Pingping","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Cong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhen","family":"He","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6517-4373","authenticated-orcid":false,"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,5]]},"reference":[{"key":"1044_CR1","doi-asserted-by":"publisher","first-page":"102348","DOI":"10.1016\/j.artmed.2022.102348","volume":"131","author":"MT Abdulkhaleq","year":"2022","unstructured":"MT Abdulkhaleq TA Rashid A Alsadoon  2022 Harmony search: current studies and uses on healthcare systems Artif Intell Med 131 102348 https:\/\/doi.org\/10.1016\/j.artmed.2022.102348","journal-title":"Artif Intell Med"},{"key":"1044_CR2","doi-asserted-by":"publisher","first-page":"100090","DOI":"10.1016\/j.cmpbup.2022.100090","volume":"3","author":"MT Abdulkhaleq","year":"2023","unstructured":"MT Abdulkhaleq TA Rashid BA Hassan  2023 Fitness dependent optimizer with neural networks for COVID-19 patients Comput Methods Programs Biomed Update 3 100090 https:\/\/doi.org\/10.1016\/j.cmpbup.2022.100090","journal-title":"Comput Methods Programs Biomed Update"},{"key":"1044_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-023-08332-3","author":"JM Abdullah","year":"2023","unstructured":"JM Abdullah TA Rashid BB Maaroof  2023 Multi-objective fitness-dependent optimizer algorithm Neural Comput Appl https:\/\/doi.org\/10.1007\/s00521-023-08332-3","journal-title":"Neural Comput Appl"},{"issue":"2\u20133","key":"1044_CR4","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.jlap.2009.12.002","volume":"17","author":"J Alcala-Fdez","year":"2011","unstructured":"J Alcala-Fdez A Fernandez J Luengo  2011 KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework J Mult-Valued Log Soft Comput 17 2\u20133 255 287 https:\/\/doi.org\/10.1016\/j.jlap.2009.12.002","journal-title":"J Mult-Valued Log Soft Comput"},{"key":"1044_CR5","unstructured":"Asuncion A, Newman D (2007) UCI machine learning repository Irvine. 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