Abstract
This paper proposes a Flexible Neural Tree (FNT) model for informative gene selection and gene expression profiles classification. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using the Extended Compact Genetic Programming and the free parameters embedded in the neural tree are optimized by particle swarm optimization algorithm. Empirical results on two well-known cancer datasets shows competitive results with existing methods.
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© 2006 Springer-Verlag Berlin Heidelberg
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Chen, Y., Peng, L., Abraham, A. (2006). Gene Expression Profiling Using Flexible Neural Trees. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_133
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DOI: https://doi.org/10.1007/11875581_133
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
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