Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Fitness inheritance in genetic algorithms
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Genetic Algorithms in Noisy Environments
Machine Learning
Searching in the Presence of Noise
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Diagnosis Based on Genetic Algorithms and Fuzzy Logic in NPPs
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
The impact of missing data on data mining
Data mining
Evolutionary concept learning in first order logic: an overview
AI Communications
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
Mining manufacturing data using genetic algorithm-based feature set decomposition
International Journal of Intelligent Systems Technologies and Applications
Privacy-preserving data mining: A feature set partitioning approach
Information Sciences: an International Journal
Building a highly-compact and accurate associative classifier
Applied Intelligence
Tuning metaheuristics: A data mining based approach for particle swarm optimization
Expert Systems with Applications: An International Journal
The CASH algorithm-cost-sensitive attribute selection using histograms
Information Sciences: an International Journal
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A common approach to evaluating competing models in a classification context isvia accuracy on a test set or on cross-validation sets. However, this can becomputationally costly when using genetic algorithms with large datasets and thebenefits of performing a wide search are compromised by the fact that estimatesof the generalization abilities of competing models are subject to noise. Thispaper shows that clear advantages can be gained by using samples of the test setwhen evaluating competing models. Further, that applying statistical tests incombination with Occam‘s razor produces parsimonious models, matches the levelof evaluation to the state of the search and retains the speed advantages oftest set sampling.