ClasSi: Measuring Ranking Quality in the Presence of Object Classes with Similarity Information
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 tau and Spearman´s rho 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.
|Authors:||Zimmer (née Ivanescu) A., Wichterich M., Seidl T.|
|Published in:||L. Cao et al. (Eds.): New Frontiers in Applied Data Mining. PAKDD 2011 International Workshops, Shenzhen, China, Revised Selected Papers. Springer LNCS 7104.(Workshop on Quality Issues, Measures of Interestingness and Evaluation of Data Mining Models: QIMIE).|
|Publisher:||Springer Verlag Berlin Heidelberg|
|Type:||Conference papers (peer reviewed)|
|Research topic:||Data Analysis and Knowledge Extraction|