Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Forecasting with neural networks
Information and Management
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Genetic Programming and Evolvable Machines
Evolutionary rule-based system for IPO underpricing prediction
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Constructive induction and genetic algorithms for learning concepts with complex interaction
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The Genetic Kernel Support Vector Machine: Description and Evaluation
Artificial Intelligence Review
Genetic programming for attribute construction in data mining
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Inferring ECA-based rules for ambient intelligence using evolutionary feature extraction
Journal of Ambient Intelligence and Smart Environments
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When dealing with classification and regression problems, there is a strong need for high-quality attributes. This is a capital issue not only in financial problems, but in many Data Mining domains. Constructive Induction methods help to overcome this problem by mapping the original representation into a new one, where prediction becomes easier. In this work we present GPPE: a GP-based method that projects data from an original data space into another one where data approaches linear behavior (linear separability or linear regression). Also, GPPE is able to reduce the dimensionality of the problem by recombining related attributes and discarding irrelevant ones. We have applied GPPE to two financial domains: Bankruptcy prediction and IPO Underpricing prediction. In both cases GPPE automatically generated a new data representation that obtained competitive prediction rates and drastically reduced the dimensionality of the problem.