Machine Learning
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
ECML '93 Proceedings of the European Conference on Machine Learning
A Modified Classifier System Compaction Algorithm
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Classifier fitness based on accuracy
Evolutionary Computation
Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System
Learning Classifier Systems
A mixed discrete-continuous attribute list representation for large scale classification domains
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Learning concept classification rules using genetic algorithms
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Performance and efficiency of memetic pittsburgh learning classifier systems
Evolutionary Computation
Speeding up the evaluation of evolutionary learning systems using GPGPUs
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Ensemble learning classifier system and compact ruleset
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Large scale data mining using genetics-based machine learning
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Efficient training set use for blood pressure prediction in a large scale learning classifier system
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This paper proposes three post-processing operators (rule cleaning, rule pruning and rule swapping) which combined together in different ways can help reduce the complexity of decision lists evolved by means of genetics-based machine learning. While the first two operators work on the independent rules to reduce the number of expressed attributes, the last one changes the order of the rules (based on the similarities between them) to identify and delete the unnecessary ones. These operators were tested using the BioHEL system over 35 different problems. Our results show that it is possible to reduce the number of specified attributes per rule and the number rules up to 30% in some problems, without producing significant changes in the test accuracy. Moreover, the approaches presented in this paper can be easily extended to other learning paradigms and representations.