Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
For real! XCS with continuous-valued inputs
Evolutionary Computation
System-level metrics for hardware/software architectural mapping
DELTA '04 Proceedings of the Second IEEE International Workshop on Electronic Design, Test and Applications
Applications of Learning Classifier Systems
Applications of Learning Classifier Systems
Robust automatic target recognition using learning classifier systems
Information Fusion
Learning classifier systems: a survey
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Classifier fitness based on accuracy
Evolutionary Computation
Intrusion detection using a linguistic hedged fuzzy-XCS classifier system
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Using Self-Organizing Maps with Learning Classifier System for Intrusion Detection
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Intrusion detection with evolutionary learning classifier systems
Natural Computing: an international journal
CoXCS: A Coevolutionary Learning Classifier Based on Feature Space Partitioning
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
An adaptive network intrusion detection method based on PCA and support vector machines
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Resource management and scalability of the XCSF learning classifier system
Theoretical Computer Science
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XCSR is an accuracy-based learning classifier system (LCS) which can handle classification problems with real-value features. However, as the number of features increases, a high classification accuracy comes at the cost of more resources: larger population sizes and longer computational running times. In this research, we present a PCA-enhanced LCS, which uses principal component analysis (PCA) as a preprocessing step for XCSR, and examine how it performs on complex multi-dimensional real-world data. The experiments show that this technique, in addition to significantly reducing the computational resources and time requirements of XCSR, maintains its high accuracy and even occasionally improves it. In addition to that, it reduces the required population size needed by XCSR.