Classifier systems and genetic algorithms
Artificial Intelligence
Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Learning classifier system ensemble for data mining
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Post-processing operators for decision lists
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Hi-index | 0.00 |
The aim of this paper is twofold, to improve the generalization ability, and to improve the readability of learning classifier system. Firstly, an ensemble architecture of LCS (LCSE) is described in order to improve the generalization ability of the original LCS. Secondly, an algorithm is presented for compacting the final classifier population set in order to improve the readability of LCSE, which is an amendatory version of CRA brought by Wilson. Some test experiments are conducted based on the benchmark data sets of UCI repository. The experimental results show that LCSE has better generalization ability than single LCS, decision tree, neural network and their bagging methods. Comparing with the original population rulesets, compact rulesets have readily interpretable knowledge like decision tree, whereas decrease the prediction precision lightly.