{"id":1042,"date":"2023-06-28T14:12:43","date_gmt":"2023-06-28T19:12:43","guid":{"rendered":"https:\/\/www.uv.mx\/personal\/aguerra\/?p=1042"},"modified":"2023-06-28T14:12:53","modified_gmt":"2023-06-28T19:12:53","slug":"new-article-in-sofwarex","status":"publish","type":"post","link":"https:\/\/www.uv.mx\/personal\/aguerra\/2023\/06\/28\/new-article-in-sofwarex\/","title":{"rendered":"New Article in SofwareX"},"content":{"rendered":"<p>A. Platas-L\u00f3pez, \u00a0<strong>A. Guerra-Hern\u00e1ndez<\/strong>, M. Quiroz-Castellanos, N. Cruz-Ram\u00edrez. <strong><span style=\"color: #005baa\">dplbnDE: An R package for discriminative parameter learning of Bayesian Networks by Differential\u00a0<\/span><\/strong><span style=\"color: #005baa\"><b>Evolution.<\/b><\/span> SoftwareX, 23(2023) 101442, June 2023. ISSN 2352-7110 | DOI: 10.1016\/j.softx.2023.101442 | <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2352711023001383?via%3Dihub\" target=\"_blank\" rel=\"noopener\">Elsevier<\/a><\/p>\n<p><strong>Abstract:<\/strong> The dplbnDE R package is a novel tool that implements Differential Evolution strategies for training Bayesian Network parameters using Discriminative Learning. Focusing on optimizing the Conditional Log-Likelihood rather than the log-likelihood, dplbnDE enhances the performance of Bayesian Networks models in various applications. The package offers four main functions (DErand, DEbest, jade, and lshade) that implement different DE variants, providing users with a versatile and efficient approach to Bayesian Network parameter learning. dplbnDE has the potential to impact data-driven industries by improving predictive capabilities and decision-making processes in fields such as healthcare, finance, and supply chain management. The package and its code are made freely available.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A. Platas-L\u00f3pez, \u00a0A. Guerra-Hern\u00e1ndez, M. Quiroz-Castellanos, N. Cruz-Ram\u00edrez. dplbnDE: An R package for discriminative parameter learning of Bayesian Networks by Differential\u00a0Evolution. SoftwareX, 23(2023) 101442, June 2023. ISSN 2352-7110 | DOI: 10.1016\/j.softx.2023.101442 | Elsevier Abstract: The dplbnDE R package is a novel tool that implements Differential Evolution strategies for training Bayesian Network parameters using Discriminative Learning&#8230;.<\/p>\n","protected":false},"author":1701,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-1042","post","type-post","status-publish","format-standard","hentry","category-miblog"],"_links":{"self":[{"href":"https:\/\/www.uv.mx\/personal\/aguerra\/wp-json\/wp\/v2\/posts\/1042","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.uv.mx\/personal\/aguerra\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.uv.mx\/personal\/aguerra\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.uv.mx\/personal\/aguerra\/wp-json\/wp\/v2\/users\/1701"}],"replies":[{"embeddable":true,"href":"https:\/\/www.uv.mx\/personal\/aguerra\/wp-json\/wp\/v2\/comments?post=1042"}],"version-history":[{"count":1,"href":"https:\/\/www.uv.mx\/personal\/aguerra\/wp-json\/wp\/v2\/posts\/1042\/revisions"}],"predecessor-version":[{"id":1045,"href":"https:\/\/www.uv.mx\/personal\/aguerra\/wp-json\/wp\/v2\/posts\/1042\/revisions\/1045"}],"wp:attachment":[{"href":"https:\/\/www.uv.mx\/personal\/aguerra\/wp-json\/wp\/v2\/media?parent=1042"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.uv.mx\/personal\/aguerra\/wp-json\/wp\/v2\/categories?post=1042"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.uv.mx\/personal\/aguerra\/wp-json\/wp\/v2\/tags?post=1042"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}