A hybrid system based on information gain and principal component analysis for the classification of transcranial Doppler signals
Küçük Resim Yok
Tarih
2012
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
ELSEVIER IRELAND LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
A transcranial Doppler (TCD) is a non-invasive, easy to apply and reliable technique which is used in the diagnosis of various brain diseases by measuring the blood flow velocities in brain arteries. This study aimed to classify the TCD signals, and feature ranking (information gain - IG) and dimension reduction methods (principal component analysis - PCA) were used as a hybrid to improve the classification efficiency and accuracy. In this context, each feature within the feature space was ranked depending on its importance for the classification using the IG method. Thus, the less important features were ignored and the highly important features were selected. Then, the PCA method was applied to the highly important features for dimension reduction. As a result, a hybrid feature reduction between the selection of the highly important features and the application of the PCA method on the reduced features were achieved. To evaluate the effectiveness of the proposed method, experiments were conducted using a support vector machine (SVM) classifier on the TCD signals recorded from the temporal region of the brain of 82 patients, as well as 24 healthy people. The experimental results showed that using the IG and PCA methods as a hybrid improves the classification efficiency and accuracy compared with individual usage. (C) 2011 Elsevier Ireland Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
Feature selection, Principal component analysis, Information gain, Discrete wavelet transform, Support vector machine
Kaynak
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
107
Sayı
3
