Projects per year
Abstract
Subgroup Discovery is the process of finding and describing sufficiently large subsets of a given population that have unusual distributional characteristics with regard to some target attribute. Such subgroups can be used as a statistical summary which improves on the default summary of stating the overall distribution in the population. A natural way to evaluate such summaries is to quantify the difference between predicted and empirical distribution of the target. In this paper we propose to use proper scoring rules, a wellknown family of evaluation measures for assessing the goodness of probability estimators, to obtain theoretically wellfounded evaluation measures for subgroup discovery. From this perspective, one subgroup is better than another if it has lower divergence of target probability estimates from the actual labels on average. We demonstrate empirically on both synthetic and realworld data that this leads to higher quality statistical summaries than the existing methods based on measures such as Weighted Relative Accuracy.
Original language  English 

Title of host publication  Machine Learning and Knowledge Discovery in Databases 
Subtitle of host publication  European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 1923, 2016, Proceedings, Part II 
Publisher  Springer 
Pages  492510 
Number of pages  19 
ISBN (Electronic)  9783319462271 
ISBN (Print)  9783319462264 
DOIs  
Publication status  Published  2016 
Publication series
Name  Lecture Notes in Computer Science 

Publisher  Springer 
Volume  9852 
ISSN (Print)  03029743 
Structured keywords
 Jean Golding
 SPHERE
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Dive into the research topics of 'Subgroup Discovery with Proper Scoring Rules'. Together they form a unique fingerprint.Projects
 2 Finished

SPHERE (EPSRC IRC)
Craddock, I. J., Coyle, D. T., Flach, P. A., Kaleshi, D., Mirmehdi, M., Piechocki, R. J., Stark, B. H., Ascione, R., Ashburn, A. M., Burnett, M. E., Damen, D., GoobermanHill, R. J. S., Harwin, W. S., Hilton, G., Holderbaum, W., Holley, A. P., Manchester, V. A., Meller, B. J., Stack, E. & Gilchrist, I. D.
1/10/13 → 30/09/18
Project: Research, Parent
