Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Machine Learning
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Improving Supervised Learning by Feature Decomposition
FoIKS '02 Proceedings of the Second International Symposium on Foundations of Information and Knowledge Systems
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
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Comparison of neural networks and discriminant analysis in predicting forest cover types
Comparison of neural networks and discriminant analysis in predicting forest cover types
Decomposition Methodology For Knowledge Discovery And Data Mining: Theory And Applications (Machine Perception and Artificial Intelligence)
DXCS: an XCS system for distributed data mining
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
Binary rule encoding schemes: a study using the compact classifier system
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Neural-Based Learning Classifier Systems
IEEE Transactions on Knowledge and Data Engineering
Adaptive mixtures of local experts
Neural Computation
Backpropagation applied to handwritten zip code recognition
Neural Computation
Ensemble techniques for parallel genetic programming based classifiers
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
An adaptive nearest neighbor classification algorithm for data streams
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Can evolutionary computation handle large datasets? a study into network intrusion detection
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Analysis of CCME: Coevolutionary Dynamics, Automatic Problem Decomposition, and Regularization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An adaptive knowledge evolution strategy for finding near-optimal solutions of specific problems
Expert Systems with Applications: An International Journal
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Learning classifier systems (LCSs) are rule-based inductive learning systems that have been widely used in the field of supervised and reinforcement learning over the last few years. This paper employs sUpervised Classifier System (UCS), a supervised learning classifier system, that was introduced in 2003 for classification tasks in data mining. We present an adaptive framework of UCS on top of a self-organized map (SOM) neural network. The overall classification problem is decomposed adaptively and in real time by the SOM into subproblems, each of which is handled by a separate UCS. The framework is also tested with replacing UCS by a feedforward artificial neural network (ANN). Experiments on several synthetic and real data sets, including a very large real data set, show that the accuracy of classifications in the proposed distributed environment is as good or better than in the nondistributed environment, and execution is faster. In general, each UCS attached to a cell in the SOM has a much smaller population size than a single UCS working on the overall problem; since each data instance is exposed to a smaller population size than in the single population approach, the throughput of the overall system increases. The experiments show that the proposed framework can decompose a problem adaptively into subproblems, maintaining or improving accuracy and increasing speed.