A Nearest Hyperrectangle Learning Method
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
A hybrid nearest-neighbor and nearest-hyperrectangle algorithm
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Data mining with decision trees and decision rules
Future Generation Computer Systems - Special double issue on data mining
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Artificial Intelligence Review - Special issue on lazy learning
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In Nearest Rectangle (NR) learning, training instances are generalized into hyperrectangles and a query is classified according to the class of its nearest rectangle. The method has not received much attention since its introduction mainly because, as a hybrid learner, it does not gain accuracy advantage while sacrificing classification time comparing to some other interpretable eager learners such as decision trees. In this paper, we seek for accuracy improvement of NR learning through controlling the generation of rectangles, so that each of them has the right of inference. Rectangles having the right of inference are compact, conservative, and good for making local decisions. Experiments on benchmark datasets validate the effectiveness of the proposed approach.