Data mining: concepts and techniques
Data mining: concepts and techniques
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Learning Logical Definitions from Relations
Machine Learning
A New MDL Measure for Robust Rule Induction (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Incremental learning of linear model trees
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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A new technique, SNIPER, is proposed for learning a model that deals with continuous values of exceptionality. Specifically, given some training objects associated with a continuous attribute F, SNIPER induces a rule-based model for the identification of those objects likely to score the maximum values for F. The purpose of SNIPER differs from the one pursued in regression problems, since its main objective is to retrieve those objects more likely to score the highest values of F. Although there are opportunities for improvement, the results of a preliminary evaluation are encouraging. SNIPER is competitive in the quality of the attained results with respect to some established competitors, while outperforming them when the exceptional objects are very rare. Additionally, SNIPER is much faster in the induction of a model of object exceptionality.