Different slopes for different folks: mining for exceptional regression models with cook's distance
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Generic pattern trees for exhaustive exceptional model mining
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Multi-label lego -- enhancing multi-label classifiers with local patterns
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
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Whenever a dataset has multiple discrete target variables, we want our algorithms to consider not only the variables themselves, but also the interdependencies between them. We propose to use these interdependencies to quantify the quality of subgroups, by integrating Bayesian networks with the Exceptional Model Mining framework. Within this framework, candidate subgroups are generated. For each candidate, we fit a Bayesian network on the target variables. Then we compare the network’s structure to the structure of the Bayesian network fitted on the whole dataset. To perform this comparison, we define an edit distance-based distance metric that is appropriate for Bayesian networks. We show interesting subgroups that we experimentally found with our method on datasets from music theory, semantic scene classification, biology and zoogeography.