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Applying Machine Learning Diversity Metrics to Data Fusion in Information Retrieval

  • David Leonard
  • David Lillis
  • Lusheng Zhang
  • Fergus Toolan
  • Rem W. Collier
  • John Dunnion
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6611)

Abstract

The Supervised Machine Learning task of classification has parallels with Information Retrieval (IR): in each case, items (documents in the case of IR) are required to be categorised into discrete classes (relevant or non-relevant). Thus a parallel can also be drawn between classifier ensembles, where evidence from multiple classifiers are combined to achieve a superior result, and the IR data fusion task.

This paper presents preliminary experimental results on the applicability of classifier ensemble diversity metrics in data fusion. Initial results indicate a relationship between the quality of the fused result set (as measured by MAP) and the diversity of its inputs.

Keywords

Information Retrieval Relevant Document Data Fusion Entropy Measure Classifier Ensemble 
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 2011

Authors and Affiliations

  • David Leonard
    • 1
  • David Lillis
    • 1
  • Lusheng Zhang
    • 1
  • Fergus Toolan
    • 1
  • Rem W. Collier
    • 1
  • John Dunnion
    • 1
  1. 1.School of Computer Science and InformaticsUniversity College DublinIreland

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