Applying Machine Learning Diversity Metrics to Data Fusion in Information Retrieval

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.

Authors and Affiliations

1. School of Computer Science and Informatics, University College Dublin, Ireland

   David Leonard, David Lillis, Lusheng Zhang, Fergus Toolan, Rem W. Collier & John Dunnion

Editors and Affiliations

1. Information School, University of Sheffield, Regent Court, 211 Portobello Street, S1 4DP, Sheffield, UK

   Paul Clough

2. CLARITY: Centre for Sensor Web Technologies, School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland

   Colum Foley , Cathal Gurrin  & Hyowon Lee ,  & 

3. Centre for Next Generation Localisation, School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland

   Gareth J. F. Jones

4. TNO Human Factors, Brassersplein 2, 2612 CT, Delft, The Netherlands

   Wessel Kraaij

5. Yahoo! Research, 177 Diagonal, 08018, Barcelona, Spain

   Vanessa Mudoch

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