A multiple testing procedure for input variable selection in neural networks

In this paper a novel procedure to select the input nodes in neural network modeling is presented and discussed. The approach is developed in a multiple testing framework and so it is able to take under control the well known data snooping problem which arises when the same sample is used more than once for estimation and model selection.

• Type: Conference
• Year: 2008
• Where: ESANN
• Authors: Michele La Rocca, Cira Perna
• Keywords: Data Snooping Problem, ESANN 2008, Multiple Testing Framework, Neural Network Modeling, Neural Networks