Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Genetic Programming for Feature Discovery and Image Discrimination
Proceedings of the 5th International Conference on Genetic Algorithms
Effects of Different Types of New Attribute on Constructive Induction
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Genetic programming for attribute construction in data mining
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Fitness Function Comparison for GA-Based Feature Construction
Current Topics in Artificial Intelligence
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
G3P-MI: A genetic programming algorithm for multiple instance learning
Information Sciences: an International Journal
Feature set reduction by evolutionary selection and construction
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
Multi-objective genetic programming for visual analytics
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Strengthening learning algorithms by feature discovery
Information Sciences: an International Journal
Evolutionary search of optimal features
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Inferring ECA-based rules for ambient intelligence using evolutionary feature extraction
Journal of Ambient Intelligence and Smart Environments
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Feature construction in classification is a preprocessing step in which one or more new attributes are constructed from the original attribute set, the object being to construct features that are more predictive than the original feature set. Genetic programming allows the construction of nonlinear combinations of the original features. We present a comprehensive analysis of genetic programming (GP) used for feature construction, in which four different fitness functions are used by the GP and four different classification techniques are subsequently used to build the classifier. Comparisons are made of the error rates and the size and complexity of the resulting trees. We also compare the overall performance of GP in feature construction with that of GP used directly to evolve a decision tree classifier, with the former proving to be a more effective use of the evolutionary paradigm.