Comparison of a new ad-hoc classification method with Support Vector Machine and ensemble classifiers for the diagnosis of Meniere's disease using EVestG signals

Z. A. Dastgheib, O. Ranjbar Pouya, B. Lithgow, Z. Moussavi

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4 Citations (Scopus)


In this paper, we compared the performance of our previously designed ad-hoc classifier with Support Vector Machine (SVM) and a family of ensemble learners on classification of patients with Meniere's disease (MD) based on Electrovestibulography (EVestG) signals. The ad-hoc classifier was developed based on the average vote of classifiers each built for a single feature using Linear Discriminant analysis (LDA). The training and test datasets included EVestG signals recorded from 14 MD patients and 16 age-matched healthy controls for training and 9 MD patients and 10 age-matched controls for test dataset. The feature space was built based on the EVestG characteristic signals produced in response to side tilt stimulation. The most discriminative features of the training set were selected using the minimum-redundancy-maximum-relevancy (mRMR) algorithm following one-way analysis of variance (ANOVA). SVM and three ensemble methods, including Bagging, Adaptive Boosting (AdaBoost) and Random Subspace methods were used for comparing the classification performance with that of our ad-hoc voting classifier. The classification results on the test data set showed that the ad-hoc voting classifier outperformed the competitor algorithms in terms of sensitivity, specificity and overall accuracy. The implications of the results are discussed.

Original languageEnglish
Title of host publication2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781467387217
Publication statusPublished - 31 Oct 2016
Externally publishedYes
EventIEEE Canadian Conference on Electrical and Computer Engineering 2016 - Vancouver, Canada
Duration: 14 May 201618 May 2016

Publication series

NameCanadian Conference on Electrical and Computer Engineering
ISSN (Print)0840-7789


ConferenceIEEE Canadian Conference on Electrical and Computer Engineering 2016
Abbreviated titleCCECE 2016
Internet address

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