Machine Learning - Special issue on inductive transfer
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Mining Changes for Real-Life Applications
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
An Interior Trust Region Approach for Nonlinear Minimization Subject to Bounds
An Interior Trust Region Approach for Nonlinear Minimization Subject to Bounds
On detecting differences between groups
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
Mining changing regions from access-constrained snapshots: a cluster-embedded decision tree approach
Journal of Intelligent Information Systems
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Constrained Clustering: Advances in Algorithms, Theory, and Applications
The Journal of Machine Learning Research
Expert-guided subgroup discovery: methodology and application
Journal of Artificial Intelligence Research
Differential Predictive Modeling for Racial Disparities in Breast Cancer
BIBM '09 Proceedings of the 2009 IEEE International Conference on Bioinformatics and Biomedicine
IEEE Transactions on Knowledge and Data Engineering
Differential biclustering for gene expression analysis
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
A random method for quantifying changing distributions in data streams
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Mining Low-Support Discriminative Patterns from Dense and High-Dimensional Data
IEEE Transactions on Knowledge and Data Engineering
A constrained frequent pattern mining system for handling aggregate constraints
Proceedings of the 16th International Database Engineering & Applications Sysmposium
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Analyzing differences in multivariate datasets is a challenging problem. This topic was earlier studied by finding changes in the distribution differences either in the form of patterns representing conjunction of attribute value pairs or univariate statistical analysis for each attribute in order to highlight the differences. All such methods focus only on change in attributes in some form and do not implicitly consider the class labels associated with the data. In this paper, we pose the difference in distribution in a supervised scenario where the change in the data distribution is measured in terms of the change in the corresponding classification boundary. We propose a new constrained logistic regression model to measure such a difference between multivariate data distributions based on the predictive models induced on them. Using our constrained models, we measure the difference in the data distributions using the changes in the classification boundary of these models. We demonstrate the advantages of the proposed work over other methods available in the literature using both synthetic and real-world datasets.