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Similarity Weighted Ensembles for Relocating Models of Rare Events

  • Claire D’Este
  • Ashfaqur Rahman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7872)

Abstract

Spatially distributed regions may have different influences that affect the underlying physical processes and make it inappropriate to directly relocate learned models. We may also be aiming to detect rare events for which we have examples in some regions, but not others. A novel method is presented for combining classifiers trained on regions with known sensor data and predicting rare events in new regions, specifically the closure of shellfish farms. The proposed similarity weighted ensemble method demonstrates an average 10 fold improvement in accuracy over One Class classification and 3 fold improvement over rules hand-crafted by an expert.

Keywords

Matthews Correlation Faecal Bacterium Fold Improvement National Weather Service Practical Salinity Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Claire D’Este
    • 1
  • Ashfaqur Rahman
    • 1
  1. 1.Intelligent Sensing and Systems LaboratoryCSIRO Castray EsplanadeHobartAustralia

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