{"id":1832,"date":"2017-03-18T20:55:58","date_gmt":"2017-03-19T01:55:58","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=1832"},"modified":"2026-02-18T11:29:02","modified_gmt":"2026-02-18T16:29:02","slug":"anderson2017binary","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2017\/03\/18\/anderson2017binary\/","title":{"rendered":"Binary Fuzzy Measures and Choquet Integration for Multi-Source Fusion"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>Countless challenges in engineering require the intelligent combining (aka fusion) of data or information from multiple sources. The Choquet integral (ChI), a parametric aggregation function, is a well-known tool for multisource fusion, where source refers to sensors, humans and\/or algorithms. In particular, a selling point of the ChI is its ability to model and subsequently exploit rich interactions between inputs. For a task with N inputs, the ChI has 2^N interaction variables. Therefore, the ChI becomes intractable quickly in terms of storage and its data-driven learning. Herein, we study the properties of an efficient to store, compute, and ultimately optimize version of the ChI based on a binary fuzzy measure (BFM). The BFM is further motivated by empirical observations in the areas of multi-sensor fusion and hyperspectral image processing. Herein, we provide a deeper understanding of the inner workings, capabilities and underlying philosophy of a BM ChI (BChI). We also prove that two fuzzy integrals, the ChI and the Sugeno integral, are equivalent for a BFM. Furthermore, only a small subset of BFM variables need be stored, which reduces the BChI to a relatively simple look up operation. <\/p>\n<h2>Links:<\/h2>\n<p>  <a href=\"https:\/\/github.com\/GatorSense\/Publications\/blob\/master\/Anderson2017Binary.pdf\"><img decoding=\"async\" border=\"2\" alt=\"PDF\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/pdflogo-e1482256801729.png\" height=\"50\"><\/a><\/p>\n<h2>Citation:<\/h2>\n<pre><code>D. T. Anderson, M. A. Islam, R. King, N. H. Younan, J. R. Fairley, S. Howington, F. Petry, P. Elmore and A. Zare, \"Binary Fuzzy Measures and Choquet Integration for Multi-Source Fusion,\" Intl. Conf. on Military Technologies 2017. <\/code><\/pre>\n<pre><code>@InProceedings{Anderson2017Binary,\nTitle = {Binary Fuzzy Measures and Choquet Integration for Multi-Source Fusion},\nAuthor = {Derek T. Anderson and Muhammad Aminul Islam and Roger King and Nicolas H. Younan and Joshua R. Fairley and Stacy Howington and Frederick Petry and Paul Elmore and Alina Zare},\nBooktitle = {Intl. Conf. on Military Technologies},\nYear = {2017}\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: Countless challenges in engineering require the intelligent combining (aka fusion) of data or information from multiple sources. The Choquet integral (ChI), a parametric aggregation function, is a well-known tool for multisource fusion, where source refers to sensors, humans and\/or algorithms. In particular, a selling point of the ChI is its ability to model and [&hellip;]<\/p>\n","protected":false},"author":28,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-templates\/single-sidebar-none.php","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"featured_post":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[17],"tags":[145,151,313,365,413,451,605],"class_list":["post-1832","post","type-post","status-publish","format-standard","hentry","category-conference_paper","tag-choquet-integral","tag-classification","tag-fusion","tag-hyperspectral","tag-landmine","tag-metal-detector","tag-radar"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1832","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/users\/28"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/comments?post=1832"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1832\/revisions"}],"predecessor-version":[{"id":14979,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1832\/revisions\/14979"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=1832"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=1832"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=1832"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}