Neural networks and the bias/variance dilemma
Neural Computation
Mathematical methods in artificial intelligence
Mathematical methods in artificial intelligence
Elements of artificial neural networks
Elements of artificial neural networks
Neural Networks for Statistical Modeling
Neural Networks for Statistical Modeling
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Genetic Algorithms and Genetic Programming in Computational Finance
Genetic Algorithms and Genetic Programming in Computational Finance
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Random sampling technique for overfitting control in genetic programming
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
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Motivated by a measure of predictability, this paper uses the extracted signal ratio as a measure of the degree of overfitting. With this measure, we examine the performance of one type of overfitting-avoidance design frequently used in financial applications of GP. Based on the simulation results run with the software Simple GP, we find that this design is not effective in avoiding overfitting. Furthermore, within the range of search intensity typically considered by these applications, we find that underfitting, instead of overfitting, is the more prevalent problem. This problem becomes more serious when the data is generated by a process that has a high degree of algorithmic complexity. This paper, therefore, casts doubt on the conclusions made by those early applications regarding the poor performance of GP, and recommends that changes be made to ensure progress.