Abstract
The artificial neural networks are an imitation of human brain architecture. Dendritic Computing is based on the concept that dendrites are the basic building blocks for a wide range of nervous systems. Dendritic Computing has been proved to produce perfect approximation of any data distribution. This result guarantees perfect accuracy training. However, we have found great performance degradation when tested on conventional k-fold cross-validation schemes. In this paper we propose to modify the basic strategy of hyperbox definition in DC introducing a factor of reduction of these hyperboxes.We obtain a big increase in classification performance applying with this schema over a database of features extracted from Magnetic Resonance Imaging (MRI) including Alzheimer’s Disease (AD) patients and control subjects.
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Chyzhyk, D., Graña, M. (2011). Optimal Hyperbox Shrinking in Dendritic Computing Applied to Alzheimer’s Disease Detection in MRI. In: Corchado, E., Snášel, V., Sedano, J., Hassanien, A.E., Calvo, J.L., Ślȩzak, D. (eds) Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011. Advances in Intelligent and Soft Computing, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19644-7_57
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DOI: https://doi.org/10.1007/978-3-642-19644-7_57
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