Journal article Open Access

Semi-Supervised Online Structure Learning for Composite Event Recognition

Evangelos Michelioudakis; Alexander Artikis; Georgios Paliouras


Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Evangelos Michelioudakis</dc:creator>
  <dc:creator>Alexander Artikis</dc:creator>
  <dc:creator>Georgios Paliouras</dc:creator>
  <dc:date>2019-01-16</dc:date>
  <dc:description>Online structure learning approaches, such as those stemming from Statistical Relational Learning, enable the discovery of complex relations in noisy data streams. However, these methods assume the existence of fully-labelled training data, which is unrealistic for most real-world applications. We present a novel approach for completing the supervision of a semi-supervised structure learning task. We incorporate graph-cut minimisation, a technique that derives labels for unlabelled data, based on their distance to their labelled counterparts. In order to adapt graph-cut minimisation to first order logic, we employ a suitable structural distance for measuring the distance between sets of logical atoms. The labelling process is achieved online (single-pass) by means of a caching mechanism and the Hoeffding bound, a statistical tool to approximate globally-optimal decisions from locally-optimal ones. We evaluate our approach on the task of composite event recognition by using a benchmark dataset for human activity recognition, as well as a real dataset for maritime monitoring. The evaluation suggests that our approach can effectively complete the missing labels and eventually, improve the accuracy of the underlying structure learning system.</dc:description>
  <dc:identifier>https://zenodo.org/record/2541610</dc:identifier>
  <dc:identifier>10.5281/zenodo.2541610</dc:identifier>
  <dc:identifier>oai:zenodo.org:2541610</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/687591/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.2541609</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/h2020_datacron</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>Semi-Supervised Online Structure Learning for Composite Event Recognition</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
</oai_dc:dc>
59
34
views
downloads
All versions This version
Views 5959
Downloads 3434
Data volume 22.3 MB22.3 MB
Unique views 5555
Unique downloads 3232

Share

Cite as