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.
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© 2009 Springer-Verlag Berlin Heidelberg
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Tax, D.M.J., Loog, M., Duin, R.P.W. (2009). Optimal Mean-Precision Classifier. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2009. Lecture Notes in Computer Science, vol 5519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02326-2_8
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DOI: https://doi.org/10.1007/978-3-642-02326-2_8
Publisher Name: Springer, Berlin, Heidelberg
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