Exemplar-Based Learning Classifier System with Dynamic Matching Range for Imbalanced Data
- 20 September 2017
- journal article
- Published by Fuji Technology Press Ltd. in Journal of Advanced Computational Intelligence and Intelligent Informatics
- Vol. 21 (5), 868-875
- https://doi.org/10.20965/jaciii.2017.p0868
Abstract
In this paper, we propose a method to improve ECS-DMR which enables appropriate output for imbalanced data sets. In order to control generalization of LCS in imbalanced data set, we propose a method of applying imbalance ratio of data set to a sigmoid function, and then, appropriately update the matching range. In comparison with our previous work (ECS-DMR), the proposed method can control the generalization of the appropriate matching range automatically to extract the exemplars that cover the given problem space, wchich consists of imbalanced data set. From the experimental results, it is suggested that the proposed method provides stable performance to imbalanced data set. The effect of the proposed method using the sigmoid function considering the data balance is shown.Keywords
This publication has 20 references indexed in Scilit:
- Dynamic matching range in Exemplar-based Learning Classifier SystemPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- Exemplar-Based Direct Policy Search with Evolutionary OptimizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Neuroevolution for reinforcement learning using evolution strategiesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Evolving Neural Networks through Augmenting TopologiesEvolutionary Computation, 2002
- Classifiers that approximate functionsNatural Computing, 2002
- Completely Derandomized Self-Adaptation in Evolution StrategiesEvolutionary Computation, 2001
- Evolutionary Algorithms for Reinforcement LearningJournal of Artificial Intelligence Research, 1999
- Classifier Fitness Based on AccuracyEvolutionary Computation, 1995
- ZCS: A Zeroth Level Classifier SystemEvolutionary Computation, 1994
- Learning to predict by the methods of temporal differencesMachine Learning, 1988