Training a 3-node neural network is NP-complete
COLT '88 Proceedings of the first annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Machine Learning
Discovering patterns in spatial data using evolutionary programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
Fuzzy min-max neural networks -- Part 2: Clustering
IEEE Transactions on Fuzzy Systems
A Semiquantitative Approach to Study Semiqualitative Systems
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
A Preliminary Investigation of Modified XCS as a Generic Data Mining Tool
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
CHIRP: a new classifier based on composite hypercubes on iterated random projections
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on the Best of SIGKDD 2011
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This article describes a new system for learning rules using rotated hyperboxes as individuals of a genetic algorithm (GA). Our method attempts to find out hyperboxes at any orientation by combining deterministic hill-climbing with GA. Standard techniques, such as C4.5, use hyperboxes that are aligned with the coordinate axes. The system uses the decision queue (DQ) as method of representing the rule set. It means that the obtained rules must be applied in specific order, that is, an example will be classify by the i-rule only if it doesn't satisfy the condition part of the i-1 previous rules. With this policy, the number of rules is less because the rules could be one inside of another one. We have tested our system on real data from UCI repository. Moreover, we have designed some two-dimensional artificial databases to show graphically the experiments. The results are summarized in the last section.