Relevance metrics to reduce input dimensions in artificial neural networks

Authors:
M. Héctor F. Satizábal;Andres Pérez-Uribe
Affiliations:
Université de Lausanne, Hautes Etudes Commerciales, Institut des Systèmes d'Information and University of Applied Sciences of Western Switzerland, HEIG-VD, REDS and Corporación BIOT ...;University of Applied Sciences of Western Switzerland, HEIG-VD, REDS
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

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Abstract

The reduction of input dimensionality is an important subject in modelling, knowledge discovery and data mining. Indeed, an appropriate combination of inputs is desirable in order to obtain better generalisation capabilities with the models. There are several approaches to perform input selection. In this work we will deal with techniques guided by measures of input relevance or input sensitivity. Six strategies to assess input relevance were tested over four benchmark datasets using a backward selection wrapper. The results show that a group of techniques produces input combinations with better generalisation capabilities even if the implemented wrapper does not compute any measure of generalisation performance.