Mediterranean Journal of Modeling and Simulation
Volume 6, Numéro 1, Pages 001-011
2016-09-30
Authors : Benselama Zoubir Abdeslem . Bencherif Mohamed A. . Guessoum Abderrezak . Mekhtiche Mohamed A. .
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.
Autism; Pathology; Speech disorder; Feature selection; Ensemble classifiers.
Magnossão De Paula Angel Felipe
.
Bensalem Imene
.
Rosso Paolo
.
Zaghouani Wajdi
.
pages 7-12.
A. Brezini
.
M. Sebbani
.
pages 52-55.
A. Brezini
.
M. Sebbani
.
pages 56-61.