Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Cancer classification using gene expression data
Information Systems - Special issue: Data management in bioinformatics
Genetic Programming and Evolvable Machines
Artificial Intelligence in Medicine
A genetic embedded approach for gene selection and classification of microarray data
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
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Discovering the models explaining the hidden relationship between genetic material and tumor pathologies is one of the most important open challenges in biology and medicine. Given the large amount of data made available by the DNA Microarray technique, Machine Learning is becoming a popular tool for this kind of investigations. In the last few years, we have been particularly involved in the study of Genetic Programming for mining large sets of biomedical data. In this paper, we present a comparison between four variants of Genetic Programming for the classification of two different oncologic datasets: the first one contains data from healthy colon tissues and colon tissues affected by cancer; the second one contains data from patients affected by two kinds of leukemia (acute myeloid leukemia and acute lymphoblastic leukemia). We report experimental results obtained using two different fitness criteria: the receiver operating characteristic and the percentage of correctly classified instances. These results, and their comparison with the ones obtained by three nonevolutionary Machine Learning methods (Support Vector Machines, MultiBoosting, and Random Forests) on the same data, seem to hint that Genetic Programming is a promising technique for this kind of classification.