cation methods for autism disordered speech
Authors: Zoubir Abdeslem Benselama, Mohamed A. Bencherif, Abderrezak Guessoum, Mohamed A. Mekhtiche
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In this paper, we present the results of our investigation on Autism classifi
cation by applying ensemble classi
ers to disordered speech signals. The aim is to distinguish between Autism sub-classes by comparing an ensemble combining three decision methods, the sequential minimization optimization (SMO) algorithm, the random forests (RF), and the feature-subspace aggregating approach (Feating). The conducted experiments allowed a reduction of 30% of the feature space with an accuracy increase over the baseline of 8.66% in the development set and 6.62% in the test set.