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FLAIRS
2008
13 years 8 months ago
Evolutionary Learning of Dynamic Naive Bayesian Classifiers
Naive Bayesian classifiers work well in data sets with independent attributes. However, they perform poorly when the attributes are dependent or when there are one or more irrelev...
Miguel A. Palacios-Alonso, Carlos A. Brizuela, Lui...
GECCO
2007
Springer
163views Optimization» more  GECCO 2007»
13 years 11 months ago
Choice and development
The process of development creates a phenotype from one or more genotypes of an individual through interaction with an environment. The opportunity for development to choose a phe...
Arthur M. Farley
GECCO
2007
Springer
210views Optimization» more  GECCO 2007»
13 years 11 months ago
Mining breast cancer data with XCS
In this paper, we describe the use of a modern learning classifier system to a data mining task. In particular, in collaboration with a medical specialist, we apply XCS to a prima...
Faten Kharbat, Larry Bull, Mohammed Odeh
GECCO
2010
Springer
191views Optimization» more  GECCO 2010»
13 years 5 months ago
Fitness importance for online evolution
To complement standard fitness functions, we propose "Fitness Importance" (FI) as a novel meta-heuristic for online learning systems. We define FI and show how it can be...
Philip Valencia, Raja Jurdak, Peter Lindsay
FSS
2010
147views more  FSS 2010»
13 years 4 months ago
A divide and conquer method for learning large Fuzzy Cognitive Maps
Fuzzy Cognitive Maps (FCMs) are a convenient tool for modeling and simulating dynamic systems. FCMs were applied in a large number of diverse areas and have already gained momentu...
Wojciech Stach, Lukasz A. Kurgan, Witold Pedrycz