Mediterranean Journal of Modeling and Simulation
Volume 6, Numéro 1, Pages 001-011
2016-09-30
Ensemble Classification Methods For Autism Disordered Speech
Authors : Benselama Zoubir Abdeslem . Bencherif Mohamed A. . Guessoum Abderrezak . Mekhtiche Mohamed A. .
Abstract
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.
Keywords
Autism; Pathology; Speech disorder; Feature selection; Ensemble classifiers.
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