Classifier systems and genetic algorithms
Artificial Intelligence
A critical review of classifier systems
Proceedings of the third international conference on Genetic algorithms
Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The class imbalance problem: A systematic study
Intelligent Data Analysis
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Data mining in learning classifier systems: comparing XCS with GAssist
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Detection of LSB steganography via sample pair analysis
IEEE Transactions on Signal Processing
Learning Classifier System Ensembles With Rule-Sharing
IEEE Transactions on Evolutionary Computation
Genetic algorithm based methodology for breaking the steganalytic systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Clustering with XCS on Complex Structure Dataset
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Clustering with XCS and agglomerative rule merging
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Self-adaptation of parameters in a learning classifier system ensemble machine
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
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This paper presents a learning classifier system ensemble for knowledge discovery from incremental data. The new ensemble was designed with a two-level architecture to improve the generalization ability. The new incoming cases are first bootstrapped to generate data as inputs to the first level classical learning classifier systems. The second level contains a plurality-vote module to determine the final classification by combining the classification results of the first level learning classifier systems. Each learning classifier system in the first level consists of two major modules, a genetic algorithm module for facilitating rule-discovery and a reinforcement learning module for adjusting the strength of the corresponding rules when rewards are received from the environment. We propose a revised Wilson's compact rule algorithm for generation of the compact rule set from the population set to improve the readability of the model. Two experiments were conducted. One was data mining of medical data and the other was steganalysis of images. The experimental results have shown that the new ensemble produced better performance on incremental data mining and better generalization than the single learning classifier system and other supervised learning methods. The results also showed that the compact rules were more interpretable.