SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
ECML '93 Proceedings of the European Conference on Machine Learning
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
The compact classifier system: motivation, analysis, and first results
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Smart crossover operator with multiple parents for a Pittsburgh learning classifier system
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
New entropy model for extraction of structural information from XCS population
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Performance and efficiency of memetic pittsburgh learning classifier systems
Evolutionary Computation
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
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The identification of significant attributes is of major importance to the performance of a variety of Learning Classifier Systems including the newly-emerged Bioinformatics-oriented Hierarchical Evolutionary Learning (BioHEL) algorithm. However, the BioHEL fails to deliver on a set of synthetic datasets which are the checkerboard data mixed with Gaussian noises due to the fact the significant attributes were not successfully recognised. To address this issue, a univariate Estimation of Distribution Algorithm (EDA) technique is introduced to BioHEL which primarily builds a probabilistic model upon the outcome of the generalization and specialization operations. The probabilistic model which estimates the significance of each attribute provides guidance for the exploration of the problem space. Experiment evaluations showed that the proposed BioHEL systems achieved comparable performance to the conventional one on a number of real-world small-scale datasets. Research efforts were also made on finding the optimal parameter for the traditional and proposed BioHEL systems.