Applied multivariate statistical analysis
Applied multivariate statistical analysis
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Elements of machine learning
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Parallel and Distributed Computing: A Survey of Models, Paradigms and Approaches
Parallel and Distributed Computing: A Survey of Models, Paradigms and Approaches
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Cellular Automata and Genetic Algorithms for Parallel Problem Solving in Human Genetics
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
On the use of genetic programming for the prediction of survival in cancer
Proceedings of the 12th annual conference on Genetic and evolutionary computation
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Can neural network constraints in GP provide power to detect genes associated with human disease?
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
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New laboratory technologies have made it possible to measure the expression levels of thousands of genes simultaneously in a particular cell or tissue. The challenge for computational biologists will be to develop methods that are able to identify subsets of gene expression variables that classify cells and tissues into meaningful clinical groups. Linear discriminant analysis is a popular multivariate statistical approach for classification of observations into groups. This is because the theory is well described and the method is easy to implement and interpret. However, an important limitation is that linear discriminant functions need to be pre-specified. To address this limitation and the limitation of linearity, we developed symbolic discriminant analysis (SDA) for the automatic selection of gene expression variables and discriminant functions that can take any form. We have implemented the genetic programming machine learning methodology for optimizing SDA in parallel on a Beowulf-style computer cluster.