New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Machine Learning - Special issue on learning with probabilistic representations
The true lift model: a novel data mining approach to response modeling in database marketing
ACM SIGKDD Explorations Newsletter
Inductive inference of VL decision rules
ACM SIGART Bulletin
Propositionalization-based relational subgroup discovery with RSD
Machine Learning
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
The Life of a Logic Programming System
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
View learning for statistical relational learning: with an application to mammography
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Top-down induction of first-order logical decision trees
Artificial Intelligence
Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming
Proceedings of the 1st ACM International Health Informatics Symposium
Decision Trees for Uplift Modeling
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
A probabilistic interpretation of precision, recall and F-score, with implication for evaluation
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
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A typical classification problem involves building a model to correctly segregate instances of two or more classes. Such a model exhibits differential prediction with respect to given data subsets when its performance is significantly different over these subsets. Driven by a mammography application, we aim at learning rules that predict breast cancer stage while maximizing differential prediction over age-stratified data. In this work, we present the first multi-relational differential prediction (aka uplift modeling) system, and propose three different approaches to learn differential predictive rules within the Inductive Logic Programming framework. We first test and validate our methods on synthetic data, then apply them on a mammography dataset for breast cancer stage differential prediction rule discovery. We mine a novel rule linking calcification to in situ breast cancer in older women.