Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
Feature construction and dimension reduction using genetic programming
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
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
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multi objective genetic programming for feature construction in classification problems
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
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This paper describes a new method using genetic programming (GP) in dimension reduction for classification problems. Two issues have been considered: (a) transforming the original feature space to a set of new features (components) that are more useful in classification, (b) finding a ranking measure to select more significant features. The paper presents a new class-wise orthogonal transformation function to construct a variable terminal pool for the proposed GP system. Information entropy over class intervals is used as the ranking measure for the constructed features. The performance measure is the classification accuracy on 12 benchmark problems using constructed features in a decision tree classifier. The new approach is compared with the principle component analysis (PCA) method and the results show that the new approach outperforms the PCA method on most of the problems in terms of final classification performance and dimension reduction.