{"id":4773,"date":"2025-11-20T19:28:38","date_gmt":"2025-11-20T19:28:38","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4773"},"modified":"2025-11-20T19:28:38","modified_gmt":"2025-11-20T19:28:38","slug":"robustness-to-missing-samples-in-rf-classification-ensembles-nan-sanitation-strategies-compared","status":"publish","type":"page","link":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/?page_id=4773","title":{"rendered":"Robustness to Missing Samples in RF Classification Ensembles: NaN Sanitation Strategies Compared"},"content":{"rendered":"\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/11\/Robustness-to-Missing-Samples-in-RF-Classification-Ensembles-NaN-Sanitation-Strategies-Compared-bgilbert1984.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of Robustness to Missing Samples in RF Classification Ensembles NaN Sanitation Strategies Compared bgilbert1984.\"><\/object><a id=\"wp-block-file--media-cd6b83d0-319b-40e8-9ad1-57520e955fba\" href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/11\/Robustness-to-Missing-Samples-in-RF-Classification-Ensembles-NaN-Sanitation-Strategies-Compared-bgilbert1984.pdf\">Robustness to Missing Samples in RF Classification Ensembles NaN Sanitation Strategies Compared bgilbert1984<\/a><a href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/11\/Robustness-to-Missing-Samples-in-RF-Classification-Ensembles-NaN-Sanitation-Strategies-Compared-bgilbert1984.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-cd6b83d0-319b-40e8-9ad1-57520e955fba\">Download<\/a><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">We quantify the impact of input sanitation strategies\u2014nan_to_num, zero-padding, and linear interpolation\u2014on<br>classification error and latency under controlled NaN corruption<br>of IQ streams. We integrate sanitation hooks in temporal and<br>spectral feature builders and systematically evaluate robustness<br>across corruption ratios. Our analysis reveals that linear interpolation typically dominates at low-to-moderate corruption levels,<br>while nan_to_num offers the fastest processing but introduces<br>the most spectral distortion. We provide quantitative guidance for<br>selecting appropriate sanitation strategies based on corruption<br>characteristics and performance requirements.<br>Index Terms\u2014RF signal processing, robustness, input sanitation, ensemble methods, spectral analysis<\/p>\n\n\n\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/11\/NaN-Padding-and-Interpolation-Robustness-in-RF-Ensembles-Spectrcyde-bgilbert1984.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of NaN Padding and Interpolation Robustness in RF Ensembles Spectrcyde bgilbert1984.\"><\/object><a id=\"wp-block-file--media-d2d87d57-7a2d-4309-a9e3-ed66a11375c4\" href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/11\/NaN-Padding-and-Interpolation-Robustness-in-RF-Ensembles-Spectrcyde-bgilbert1984.pdf\">NaN Padding and Interpolation Robustness in RF Ensembles Spectrcyde bgilbert1984<\/a><a href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/11\/NaN-Padding-and-Interpolation-Robustness-in-RF-Ensembles-Spectrcyde-bgilbert1984.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-d2d87d57-7a2d-4309-a9e3-ed66a11375c4\">Download<\/a><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Radio frequency (RF) signal classification systems frequently encounter corrupted input data due to hardware failures, interference, or transmission errors. Missing samples,<br>represented as NaN (Not a Number) values in digital signal<br>processing pipelines, can propagate through feature extraction<br>and classification stages, leading to degraded performance or<br>complete system failures.<br>This paper systematically evaluates the robustness of RF<br>ensemble classifiers to input corruption, specifically focusing<br>on the impact of different NaN sanitation strategies. We inject<br>controlled corruption patterns into IQ data streams and measure the resulting effects on classification accuracy, processing<br>latency, and spectral feature quality.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Repository: <a href=\"https:\/\/github.com\/bgilbert1984\/Robustness-to-Missing-Samples-in-RF-Classification-Ensembles-NaN-Sanitation-Strategies-Compared\/tree\/main\">bgilbert1984\/Robustness-to-Missing-Samples-in-RF-Classification-Ensembles-NaN-Sanitation-Strategies-Compared: We quantify the impact of input sanitation strategies\u2014nan_to_num, zero-padding, and linear interpolation\u2014on classification error and latency under controlled NaN corruption of IQ streams. We integrate sanitation hooks in temporal and spectral feature builders and systematically evaluate robustness across corruption ratios.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We quantify the impact of input sanitation strategies\u2014nan_to_num, zero-padding, and linear interpolation\u2014onclassification error and latency under controlled NaN corruptionof IQ streams. We integrate sanitation hooks in temporal andspectral feature builders and systematically evaluate robustnessacross corruption ratios. Our analysis reveals that linear interpolation typically dominates at low-to-moderate corruption levels,while nan_to_num offers the fastest processing but introducesthe&hellip;&nbsp;<\/p>\n","protected":false},"author":2,"featured_media":3380,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"class_list":["post-4773","page","type-page","status-publish","has-post-thumbnail","hentry"],"_links":{"self":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages\/4773","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4773"}],"version-history":[{"count":0,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages\/4773\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/media\/3380"}],"wp:attachment":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4773"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}