Journal article Open Access

Semi-Supervised Online Structure Learning for Composite Event Recognition

Evangelos Michelioudakis; Alexander Artikis; Georgios Paliouras


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.2541610", 
  "language": "eng", 
  "title": "Semi-Supervised Online Structure Learning for Composite Event Recognition", 
  "issued": {
    "date-parts": [
      [
        2019, 
        1, 
        16
      ]
    ]
  }, 
  "abstract": "<p>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.</p>", 
  "author": [
    {
      "family": "Evangelos Michelioudakis"
    }, 
    {
      "family": "Alexander Artikis"
    }, 
    {
      "family": "Georgios Paliouras"
    }
  ], 
  "type": "article-journal", 
  "id": "2541610"
}
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