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Erschienen in:
2012 | OriginalPaper | Buchkapitel
ClasSi: Measuring Ranking Quality in the Presence of Object Classes with Similarity Information
verfasst von : Anca Maria Ivanescu, Marc Wichterich, Thomas Seidl
Erschienen in: New Frontiers in Applied Data Mining
Verlag: Springer Berlin Heidelberg
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The quality of rankings can be evaluated by computing their correlation to an optimal ranking. State of the art ranking correlation coefficients like Kendall’s
τ
and Spearman’s
ρ
do not allow for the user to specify similarities between differing object classes and thus treat the transposition of objects from similar classes the same way as that of objects from dissimilar classes. We propose
ClasSi
, a new ranking correlation coefficient which deals with class label rankings and employs a class distance function to model the similarities between the classes. We also introduce a graphical representation of
ClasSi
akin to the
ROC
curve
which describes how the correlation evolves throughout the ranking.