Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Advanced Engineering Mathematics: Maple Computer Guide
Advanced Engineering Mathematics: Maple Computer Guide
Machine Learning
Genetic Programming and Evolvable Machines
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Programming with a Genetic Algorithm for Feature Construction and Selection
Genetic Programming and Evolvable Machines
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Genetic programming for attribute construction in data mining
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Visual learning by coevolutionary feature synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic programming for simultaneous feature selection and classifier design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Knowledge mining with genetic programming methods for variable selection in flavor design
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Self-tuned Evolution-COnstructed features for general object recognition
Pattern Recognition
Genetic Programming and Evolvable Machines
Multi objective genetic programming for feature construction in classification problems
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
PSO for feature construction and binary classification
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A feature construction method for general object recognition
Pattern Recognition
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This paper describes a new approach to the use of genetic programming (GP) for feature construction in classification problems. Rather than wrapping a particular classifier for single feature construction as in most of the existing methods, this approach uses GP to construct multiple (high-level) features from the original features. These constructed features are then used by decision trees for classification. As feature construction is independent of classification, the fitness function is designed based on the class dispersion and entropy. This approach is examined and compared with the standard decision tree method, using the original features, and using a combination of the original features and constructed features, on 12 benchmark classification problems. The results show that the new approach outperforms the standard way of using decision trees on these problems in terms of the classification performance, dimension reduction and the learned decision tree size.