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The aim of this paper is to build a tool that able to extract the regions from a brain magnetic resonance image that discriminate healthy controls from subjects with probable dementia of the Alzheimer type. We propose the use of an Extreme Learning Machine method to select the most discriminant regions and thereafter to perform the final classification according to a majority vote decision based strategy...
This paper introduces an improvement on the recently published Hybrid Extreme Rotation Forest (HERF), consisting in the anticipative determination of the the fraction of each classifier architecture included in the ensemble. We call it AHERF. Both HERF and AHERF are heterogeneous classifier ensembles, which aim to profit from the diverse problem domain specificities of each classifier architecture...
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