ClasSi: measuring ranking quality in the presence of object classes with similarity information

Authors:
Anca Maria Ivanescu;Marc Wichterich;Thomas Seidl
Affiliations:
Data Management and Data Exploration Group, RWTH Aachen University, Aachen, Germany;Data Management and Data Exploration Group, RWTH Aachen University, Aachen, Germany;Data Management and Data Exploration Group, RWTH Aachen University, Aachen, Germany
Venue:
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Year:
2011

Citing 4
Cited 0

A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems

Machine Learning
Information Retrieval

Information Retrieval
Introduction to Information Retrieval

Introduction to Information Retrieval
A novel scalable multi-class ROC for effective visualization and computation

PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 ROCcurve which describes how the correlation evolves throughout the ranking.