By Topic

Selection of voice features to diagnose hearing impairments of children

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)
Skrypnyk, I. ; Dept. of Comput. Sci. & Inf. Syst., Jyvaskyla Univ., Finland ; Grzanka, A. ; Puuronen, S. ; Szkielkowska, A.

Real-world medical data is often heterogeneous, containing many cases and features, each of which requires different a type of processing. Generally, this means that the subsets of relevant features are different for various cases. The set of voice descriptors in the problem of hearing impairment diagnosis is an example of such a heterogeneous domain. Ensemble feature selection techniques are adopted to take into account the data heterogeneity. This paper analyses the applicability of various feature selection approaches in diagnosing hearing impairments in the context of an ensemble classification. Ensemble feature selection produces multiple classifiers for this domain, based on feature subsets derived by different feature selection approaches. In particular, we are interested in performing feature selection for each particular case, taking into consideration any hidden heterogeneity in the data. We use real-world clinical hearing impairment data and compare ensemble classification to the single-classifier technique

Published in:

Computer-Based Medical Systems, 2001. CBMS 2001. Proceedings. 14th IEEE Symposium on

Date of Conference: