Optimal Mean-Precision Classifier

Authors:
David M. Tax;Marco Loog;Robert P. Duin
Affiliations:
Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands 2628 CD;Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands 2628 CD;Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands 2628 CD
Venue:
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Year:
2009

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Abstract

For pattern recognition problems where a small set of relevant objects should be retrieved from a (very) large set of irrelevant objects, standard evaluation criteria are often insufficient. For these situations often the precision-recall curve is used. An often-employed scalar measure derived from this curve is the mean precision, that estimates the average precision over all values of the recall. This performance measure, however, is designed to be non-symmetric in the two classes and it appears not very simple to optimize. This paper presents a classifier that approximately maximizes the mean precision by a collection of simple linear classifiers.