C4.5: programs for machine learning
C4.5: programs for machine learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Strong, Stable, and Reliable Fitness Pressure in XCS due to Tournament Selection
Genetic Programming and Evolvable Machines
Exploring relationships between genotype and oral cancer development through XCS
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
A symbolic fault-prediction model based on multiobjective particle swarm optimization
Journal of Systems and Software
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Analysis of medical datasets has some specific requirements not always fulfilled by standard Machine Learning methods. In particular, heterogeneous and missing data must be tolerated, the results should be easily interpretable. Moreover, with genetic data, often the combination of two or more attributes leads to non-linear effects not detectable for each attribute on its own. We present a new ML algorithm, HCS, taking inspiration from learning classifier systems, decision trees and statistical hypothesis testing. We show the results of applying this algorithm to a well-known benchmark dataset, and to HNSCC, a dataset studying the connection between smoke and genetic patterns to the development of oral cancer.