Constructive incremental learning from only local information
Neural Computation
Classifiers that approximate functions
Natural Computing: an international journal
Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Incremental Online Learning in High Dimensions
Neural Computation
Genetic Programming and Evolvable Machines
Classifier fitness based on accuracy
Evolutionary Computation
An analysis of generalization in the xcs classifier system
Evolutionary Computation
Evolutionary rule-based systems for imbalanced data sets
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
Facetwise analysis of XCS for problems with class imbalances
IEEE Transactions on Evolutionary Computation
A comparative study: function approximation with LWPR and XCSF
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Resource management and scalability of the XCSF learning classifier system
Theoretical Computer Science
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
XCS-based versus UCS-based feature pattern classification system
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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The XCSF classifier system solves regression problems iteratively online with a population of overlapping, local approximators. We show that problem solution stability and accuracy may be lost in particular settings - mainly due to XCSF's global deletion. We introduce local deletion, which prevents these detrimental effects to large extents. We show experimentally that local deletion can prevent forgetting in various problems - particularly where the problem space is non-uniformly or non-independently sampled. While we use XCSF with hyperellipsoidal receptive fields and linear approximations herein, local deletion can be applied to any XCS version where locality can be similarly defined. For future work, we propose to apply XCSF with local deletion to unbalanced, non-uniformly distributed, locally sampled problems with complex manifold structures, within which varying target error values may be reached selectively.