International Journal of Man-Machine Studies
C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
A review of feature selection techniques in bioinformatics
Bioinformatics
AMT'10 Proceedings of the 6th international conference on Active media technology
Using feature selection approaches to find the dependent features
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Efficient EDA for large opimization problems via constraining the search space of models
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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Biologists are interested in studying the relation between the genetic diversity of a population and its fitness. We adopt the notion of entropy as a measure of genetic diversity and correlate it with fitness of an evolutionary ecosystem simulation. EcoSim is a predator-prey individual based simulation which models co-evolving sexual individuals evolving in a dynamic environment. The correlation values between entropy and fitness of all the species that ever existed during the whole simulation are presented. We show how entropy strongly correlates with fitness and investigate the factors behind this result using machine learning techniques. We build a classifier based on different species' features and successfully predict the resulting correlation value between entropy and fitness. The best features affecting the quality of classification are also being investigated.