Multilayer feedforward networks are universal approximators
Neural Networks
Synergy of clustering multiple back propagation networks
Advances in neural information processing systems 2
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
C-Net: a method for generating non-deterministic and dynamic multivariate decision trees
Knowledge and Information Systems
Properties of the Bucket Brigade
Proceedings of the 1st International Conference on Genetic Algorithms
On Using Constructivism in Neural Classifier Systems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Accuracy-based Neuro And Neuro-fuzzy Classifier Systems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
MCS '02 Proceedings of the Third International Workshop on Multiple 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
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Negative correlation learning and evolutionary design of neural network ensembles
Negative correlation learning and evolutionary design of neural network ensembles
Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Rule-based evolutionary online learning systems: learning bounds, classification, and prediction
Rule-based evolutionary online learning systems: learning bounds, classification, and prediction
Be real! XCS with continuous-valued inputs
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
The role of early stopping and population size in XCS for intrusion detection
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
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
A self-organized, distributed, and adaptive rule-based induction system
IEEE Transactions on Neural Networks
An adaptive knowledge evolution strategy for finding near-optimal solutions of specific problems
Expert Systems with Applications: An International Journal
Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems
Neural Processing Letters
Unpacking and understanding evolutionary algorithms
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Comparison of two methods for computing action values in XCS with code-fragment actions
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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UCS, a sUpervised Classifier System, is an accuracy-based evolutionary learning classifier system for data mining classification tasks. UCS works through two stages: exploration and exploitation. During the exploration phase, a population of rules is evolved in order to represent a complete solution of the target problem. The exploitation phase is responsible for applying the rule-based knowledge obtained in the first phase when the system is exposed to unseen data. The representation of a rule in UCS as a univariate classification rule can be easily seen in a symbolic form, which is easy for a human to understand and comprehend (i.e. expressive power). However, the system may generate a large number of rules to cover the input space. Artificial neural networks normally provide a more compact and accurate representation. However, it is not a straightforward task to understand the network. In this paper, we propose a novel way to incorporate neural networks into UCS. The approach offers a good compromise between compactness, expressiveness, and accuracy. By using a simple artificial neural network as the classifier's action, we obtain smaller/compact population size, better generalization, while maintaining a reasonable level of expressiveness. We also apply negative correlation learning (NCL) during the training of the resultant neural network ensemble. NCL is shown to improve the generalization of the ensemble.