Randomized Variable Elimination
The Journal of Machine Learning Research
Genetic Programming with a Genetic Algorithm for Feature Construction and Selection
Genetic Programming and Evolvable Machines
Cancer gene search with data-mining and genetic algorithms
Computers in Biology and Medicine
Improving the human readability of features constructed by genetic programming
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary consequences of coevolving targets
Evolutionary Computation
A direct evolutionary feature extraction algorithm for classifying high dimensional data
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Evolutionary discriminant feature extraction with application to face recognition
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Patient-recognition data-mining model for BCG-plus interferon immunotherapy bladder cancer treatment
Computers in Biology and Medicine
Feature construction and selection using genetic programming and a genetic algorithm
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Data mining and genetic algorithm based gene/SNP selection
Artificial Intelligence in Medicine
A maximum-margin genetic algorithm for misclassification cost minimizing feature selection problem
Expert Systems with Applications: An International Journal
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This paper describes an approach being explored to improve the usefulness of machine learning techniques to classify complex, real world data. The approach involves the use of genetic algorithms as a "front end" to a traditional tree induction system (ID3) in order to find the best feature set to be used by the induction system. This approach has been implemented and tested on difficult texture classification problems. The results are encouraging and indicate significant advantages of the presented approach.