{"id":25364,"date":"2025-07-25T09:35:27","date_gmt":"2025-07-25T07:35:27","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&#038;p=25364"},"modified":"2025-07-25T09:36:47","modified_gmt":"2025-07-25T07:36:47","slug":"stability-of-machine-learning-predictive-features-under-limited-data-2","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/stability-of-machine-learning-predictive-features-under-limited-data-2\/","title":{"rendered":"Stability of Machine Learning Predictive Features Under Limited Data"},"content":{"rendered":"\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-karol-capala-paulina-tworek-jose-sousa\">Karol Capa\u0142a, Paulina Tworek, Jose Sousa\u00a0<\/h2>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">In many fields\u2014including healthcare and biomedical sciences\u2014machine learning is increasingly used to support critical decision-making. But how reliable are these models when data is scarce or incomplete?<\/p>\n\n\n\n<p class=\" eplus-wrapper\">Autors investigate this issue by examining the stability of predictive features in machine learning models trained on limited datasets. Their study compares conventional ML approaches with a previously introduced method that leverages data abstractions to enhance learning under imperfect conditions.<\/p>\n\n\n\n<p class=\" eplus-wrapper\">The results highlight that the abstraction-based approach not only maintains strong classification performance but also ensures greater consistency in feature selection\u2014even as data availability decreases. This work demonstrates that machine learning systems can be designed to remain interpretable and robust, even in the face of data scarcity, bringing us closer to safe and autonomous AI-based decision-making in complex domains.<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: <a href=\"https:\/\/sano.science\/people\/karol-capala\/\">Karol Capa\u0142a<\/a>, <a href=\"https:\/\/sano.science\/people\/paulina-tworek\/\">Paulina Tworek<\/a>, <a href=\"https:\/\/sano.science\/people\/jose-sousa\/\">Jose Sousa<\/a><\/p>\n\n\n\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: <a href=\"https:\/\/doi.org\/10.1109\/TKDE.2025.3580671\" target=\"_blank\" rel=\"noreferrer noopener\">10.1109\/TKDE.2025.3580671<\/a><\/p>\n\n\n\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: feature stability, Classification, data abstractions, limited data,  explainability, machine learning, predictions.<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n\t\n    \n        \n\t\t\t<a href=\"https:\/\/ieeexplore.ieee.org\/document\/11039685\" target=\"_blank\" rel= \"noopener noreferrer nofollow\" class=\"button primary \">\n\n\t\t\t\t<span>\n\t\t\t\t\tREAD HERE\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n        \n    \n","protected":false},"excerpt":{"rendered":"<p>Journal paper in: IEEE Transactions on Knowledge and Data Engineering, 2025<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[14],"class_list":["post-25364","research","type-research","status-publish","hentry","research_type-publications","research_team-computational-intelligence"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - 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But how reliable are these models when data is scarce or incomplete?<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">In many fields\u2014including healthcare and biomedical sciences\u2014machine learning is increasingly used to support critical decision-making. But how reliable are these models when data is scarce or incomplete?<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-c9il9v","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Autors investigate this issue by examining the stability of predictive features in machine learning models trained on limited datasets. Their study compares conventional ML approaches with a previously introduced method that leverages data abstractions to enhance learning under imperfect conditions.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Autors investigate this issue by examining the stability of predictive features in machine learning models trained on limited datasets. Their study compares conventional ML approaches with a previously introduced method that leverages data abstractions to enhance learning under imperfect conditions.<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-61wN4F","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">The results highlight that the abstraction-based approach not only maintains strong classification performance but also ensures greater consistency in feature selection\u2014even as data availability decreases. This work demonstrates that machine learning systems can be designed to remain interpretable and robust, even in the face of data scarcity, bringing us closer to safe and autonomous AI-based decision-making in complex domains.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">The results highlight that the abstraction-based approach not only maintains strong classification performance but also ensures greater consistency in feature selection\u2014even as data availability decreases. This work demonstrates that machine learning systems can be designed to remain interpretable and robust, even in the face of data scarcity, bringing us closer to safe and autonomous AI-based decision-making in complex domains.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-oGP2hC","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-7yxA0i","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: <a href=\"https:\/\/sano.science\/people\/karol-capala\/\">Karol Capa\u0142a<\/a>, <a href=\"https:\/\/sano.science\/people\/paulina-tworek\/\">Paulina Tworek<\/a>, <a href=\"https:\/\/sano.science\/people\/jose-sousa\/\">Jose Sousa<\/a><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: <a href=\"https:\/\/sano.science\/people\/karol-capala\/\">Karol Capa\u0142a<\/a>, <a href=\"https:\/\/sano.science\/people\/paulina-tworek\/\">Paulina Tworek<\/a>, <a href=\"https:\/\/sano.science\/people\/jose-sousa\/\">Jose Sousa<\/a><\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-Fum2ss","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: <a href=\"https:\/\/doi.org\/10.1109\/TKDE.2025.3580671\" target=\"_blank\" rel=\"noreferrer noopener\">10.1109\/TKDE.2025.3580671<\/a><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: <a href=\"https:\/\/doi.org\/10.1109\/TKDE.2025.3580671\" target=\"_blank\" rel=\"noreferrer noopener\">10.1109\/TKDE.2025.3580671<\/a><\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-bvpBYN","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: feature stability, Classification, data abstractions, limited data,  explainability, machine learning, predictions.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: feature stability, Classification, data abstractions, limited data,  explainability, machine learning, predictions.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-oGP2hC","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"READ HERE","button_type":"link","url":"https:\/\/ieeexplore.ieee.org\/document\/11039685","button_style":"primary","target":"_blank","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/25364","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":6,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/25364\/revisions"}],"predecessor-version":[{"id":25371,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/25364\/revisions\/25371"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=25364"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=25364"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=25364"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}