Abstract
This paper presents an intelligent approach to classification analysis of Alzheimer disease patients. Bootstrap technique is chosen to get rid of weak point of Dendritic Classifiers (DC), which is low Specificity and improve the Accuracy at all. Dendritic Classifiers (BDC) is an ensemble of weak DC trained combining their output by majority voting. Weak DCs are trained on bootstrapped samples of the train data setting varying the depth by limit number of trees and varying number of dendrites. The classification accuracies of the combined LICA-DC, Kernel LICA-DC and BDC are compared. The experimental on T1-weighted Magnetic Resonance Imaging (MRI) images indicate that the developed method can significantly improve classification results.
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Chyzhyk, D., Graña, M. (2013). Bootstrapped Dendritic Classifiers in MRI Analysis for Alzheimer’s Disease Recognition. In: Tweedale, J.W., Jain, L.C. (eds) Advanced Techniques for Knowledge Engineering and Innovative Applications. Communications in Computer and Information Science, vol 246. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42017-7_10
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DOI: https://doi.org/10.1007/978-3-642-42017-7_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42016-0
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