{"id":1618,"date":"2023-09-06T16:37:23","date_gmt":"2023-09-06T16:37:23","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1618"},"modified":"2023-09-06T16:37:38","modified_gmt":"2023-09-06T16:37:38","slug":"vol21-no2-art1","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol21-no2\/vol21-no2-art1\/","title":{"rendered":"Local Rule-Based Explanations Method Based on Genetic Algorithms with Fitness Sharing"},"content":{"rendered":"<p>Daniel A. Santos <a href=\"https:\/\/orcid.org\/0000-0002-3096-0155\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Jos\u00e9 A. Baranauskas <a href=\"https:\/\/orcid.org\/0000-0002-7501-7187\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>&#038; Renato Tin\u00f3s <a href=\"https:\/\/orcid.org\/0000-0003-4027-8851\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a><\/p>\n<p><strong>Abstract:<\/strong> The Local Rule Based Explanations method (LORE) explains decisions of black-box classifiers by using an interpretable model (Decision Tree &#8211; DT). The DT is trained with an artificial dataset generated by  Genetic Algorithms (GAs). The primary objective of this approach is to replicate the decision boundaries of the black-box model in proximity to the instance under explanation. We show that the artificial examples generated by the GAs in LORE are not necessarily diverse. Consequently, we propose the integration of GAs with fitness sharing in LORE to generate a more diversified subset of artificial examples. The underlying motivation is to ensure that the local decision boundaries of the DT more closely resemble those of the black-box classifier. Experimental results with two classifiers (Multilayer Perceptron and Random Forests), and four classification problems, indicate that LORE with fitness sharing yields more diverse GA populations, consequently leading to improved local explanations. These findings underscore the effectiveness of incorporating fitness sharing into the LORE methodology for enhancing the explainability of black-box classifiers.<\/p>\n<p><strong>Keywords:<\/strong> Explainable Artificial Intelligence, Genetic Algorithms, Fitness Sharing.<\/p>\n<p><strong>DOI code:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol21-no2-art1\">10.21528\/lnlm-vol21-no2-art1<\/a><\/p>\n<p><strong>PDF file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2023\/12\/vol21-no2-art1.pdf\">vol21-no2-art1.pdf<\/a><\/p>\n<p><strong>BibTex file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2023\/12\/vol21-no2-art1.bib\">vol21-no2-art1.bib <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Daniel A. Santos , Jos\u00e9 A. Baranauskas &#038; Renato Tin\u00f3s Abstract: The Local Rule Based Explanations method (LORE) explains decisions of black-box classifiers by using an interpretable model (Decision Tree &#8211; DT). The DT is trained with an artificial dataset <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol21-no2\/vol21-no2-art1\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1613,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1618","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Local Rule-Based Explanations Method Based on Genetic Algorithms with Fitness Sharing - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol21-no2\/vol21-no2-art1\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Local Rule-Based Explanations Method Based on Genetic Algorithms with Fitness Sharing - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"Daniel A. Santos , Jos\u00e9 A. Baranauskas &#038; Renato Tin\u00f3s Abstract: The Local Rule Based Explanations method (LORE) explains decisions of black-box classifiers by using an interpretable model (Decision Tree &#8211; DT). The DT is trained with an artificial dataset Read More ...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol21-no2\/vol21-no2-art1\/\" \/>\n<meta property=\"og:site_name\" content=\"Learning and NonLinear Models\" \/>\n<meta property=\"article:modified_time\" content=\"2023-09-06T16:37:38+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. tempo de leitura\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minuto\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol21-no2\/vol21-no2-art1\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol21-no2\/vol21-no2-art1\/\",\"name\":\"Local Rule-Based Explanations Method Based on Genetic Algorithms with Fitness Sharing - Learning and NonLinear Models\",\"isPartOf\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol21-no2\/vol21-no2-art1\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol21-no2\/vol21-no2-art1\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\",\"datePublished\":\"2023-09-06T16:37:23+00:00\",\"dateModified\":\"2023-09-06T16:37:38+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol21-no2\/vol21-no2-art1\/#breadcrumb\"},\"inLanguage\":\"pt-BR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol21-no2\/vol21-no2-art1\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol21-no2\/vol21-no2-art1\/#primaryimage\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\",\"contentUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol21-no2\/vol21-no2-art1\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Browse issues\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Learning &#038; Nonlinear Models &#8211; L&#038;NLM &#8211; Volume 21 &#8211; N\u00famero 2\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol21-no2\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Local Rule-Based Explanations Method Based on Genetic Algorithms with Fitness Sharing\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/\",\"name\":\"Learning and NonLinear Models\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/sbia.org.br\/lnlm\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"pt-BR\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#organization\",\"name\":\"Learning and NonLinear Models\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png\",\"contentUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png\",\"width\":398,\"height\":94,\"caption\":\"Learning and NonLinear Models\"},\"image\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Local Rule-Based Explanations Method Based on Genetic Algorithms with Fitness Sharing - Learning and NonLinear Models","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol21-no2\/vol21-no2-art1\/","og_locale":"pt_BR","og_type":"article","og_title":"Local Rule-Based Explanations Method Based on Genetic Algorithms with Fitness Sharing - Learning and NonLinear Models","og_description":"Daniel A. Santos , Jos\u00e9 A. Baranauskas &#038; Renato Tin\u00f3s Abstract: The Local Rule Based Explanations method (LORE) explains decisions of black-box classifiers by using an interpretable model (Decision Tree &#8211; DT). 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