{"id":706,"date":"2024-11-05T17:31:54","date_gmt":"2024-11-05T16:31:54","guid":{"rendered":"https:\/\/idea.gm.th-koeln.de\/?p=706"},"modified":"2024-11-05T17:31:57","modified_gmt":"2024-11-05T16:31:57","slug":"new-arxiv-preprint-enhancing-feature-selection-and-interpretability-in-ai-regression-tasks-through-feature-attribution","status":"publish","type":"post","link":"https:\/\/www.spotseven.de\/?p=706","title":{"rendered":"New arXiv Preprint: Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution"},"content":{"rendered":"\n

The article<\/a> is the result of an ongoing cooperation between the Institute of Data Science, Engineering, and Analytics (IDE+A) at TH K\u00f6ln (Alexander Hinterleitner<\/a>, Thomas Bartz-Beielstein<\/a>, Richard Schulz<\/a>), and MAN Energy Solutions<\/a> (Sebastian Spengler<\/a>, Thomas Winter<\/a>, Christoph Leitenmeier<\/a>). The abstract reads as follows:<\/p>\n\n\n\n

\n

Research in Explainable Artificial Intelligence (XAI) is increasing, aiming to make deep learning models more transparent. Most XAI methods focus on justifying the decisions made by Artificial Intelligence (AI) systems in security-relevant applications. However, relatively little attention has been given to using these methods to improve the performance and robustness of deep learning algorithms. Additionally, much of the existing XAI work primarily addresses classification problems. In this study, we investigate the potential of feature attribution methods to filter out uninformative features in input data for regression problems, thereby improving the accuracy and stability of predictions. We introduce a feature selection pipeline that combines Integrated Gradients with k-means clustering to select an optimal set of variables from the initial data space. To validate the effectiveness of this approach, we apply it to a real-world industrial problem – blade vibration analysis in the development process of turbo machinery.<\/p>\n<\/blockquote>\n\n\n\n

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Absolute feature attribution values based on Integrated Gradients for the subcategories of the data. The attribution values are accumulated over the entire dataset, with different colors representing different feature categories.<\/em><\/figcaption><\/figure>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

The article explores the integration of Explainable Artificial Intelligence (XAI) in enhancing deep learning performance. Focusing on regression problems, it introduces a feature selection pipeline using Integrated Gradients and k-means clustering, applied to blade vibration analysis in turbo machinery development.<\/p>\n","protected":false},"author":3,"featured_media":708,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"The article explores the integration of Explainable Artificial Intelligence (XAI) in enhancing deep learning performance.","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false},"version":2}},"categories":[44,31,47,48,16,61,6],"tags":[60,18,35,58,59],"class_list":["post-706","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-artikel","category-hyperparameter-tuning","category-industrie","category-kooperation","category-turbo-machinery","category-published","tag-ai","tag-kooperation","tag-th-koln","tag-turbo-machinery","tag-xai",""],"acf":[],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/www.spotseven.de\/wp-content\/uploads\/2024\/11\/arxiv_feature_attribution.png?fit=2462%2C1236&ssl=1","jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack-related-posts":[],"jetpack_shortlink":"https:\/\/wp.me\/p2DCPK-bo","_links":{"self":[{"href":"https:\/\/www.spotseven.de\/index.php?rest_route=\/wp\/v2\/posts\/706","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.spotseven.de\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.spotseven.de\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.spotseven.de\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.spotseven.de\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=706"}],"version-history":[{"count":1,"href":"https:\/\/www.spotseven.de\/index.php?rest_route=\/wp\/v2\/posts\/706\/revisions"}],"predecessor-version":[{"id":709,"href":"https:\/\/www.spotseven.de\/index.php?rest_route=\/wp\/v2\/posts\/706\/revisions\/709"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.spotseven.de\/index.php?rest_route=\/wp\/v2\/media\/708"}],"wp:attachment":[{"href":"https:\/\/www.spotseven.de\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=706"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.spotseven.de\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=706"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.spotseven.de\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=706"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}