By Topic

Feature Selection Using Hybrid Evaluation Approaches Based on Genetic Algorithms

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Giraldo T, L.F. ; Grupo de Control y Procesamiento Digital de Senales, Univ. Nat. de Colombia Sede Manizales ; Delgado T, E. ; Riano, J.C. ; Castellanos D, G.

For a given set of samples, a new model is proposed to reduce input feature space, which decreases the learning time of classifiers, but also, improves the prediction accuracy according to the chosen relevance criterion. This model is constructed by decision trees and genetic algorithms, which evaluates by means of k nearest neighbor rule for classification, allowing the evolution model parameters of used genetic algorithm. The training set corresponds to the extracted features from pathological (hypernasality) and non-pathological (normal) speech, acquired from 90 children, 45 examples per class. A comparative analysis between different approaches about feature selection is performed upon experimental results, showing the feasibility of this approach in such a cases involving pathologies recognition

Published in:

Electronics, Robotics and Automotive Mechanics Conference, 2006  (Volume:2 )

Date of Conference:

Sept. 2006