Parallel genetic algorithms for a hypercube
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Knowledge Discovery in Biological Datasets Using a Hybrid Bayes Classifier/Evolutionary Algorithm
BIBE '01 Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering
Design of combinational logic circuits through an evolutionary multiobjective optimization approach
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Journal of Biomedical Informatics - Special issue: Clinical machine learning
Evolving a Bayesian classifier for ECG-based age classification in medical applications
Applied Soft Computing
An extension of the naive Bayesian classifier
Information Sciences: an International Journal
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
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This paper presents a serial and parallel genetic based learnable bayesian classifier for designing a prognostic model for metabolic syndrome. The objective of the classifier is to address the fundamental problem of finding the optimal weight in the learnable bayesian classifier, by serial GA, and minimize the response time by parallel GA. The algorithms exhibit an improved capability to eliminate spurious features from the large dataset and aid the researchers in identifying those features that are solely responsible for high prediction accuracy. The effectiveness of the classifier are demonstrated using metabolic syndrome dataset obtained from Yonchon County of Korea.