A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of components in principal component analysis: a comparison of methods
Computational Statistics & Data Analysis
The nature of statistical learning theory
The nature of statistical learning theory
Classification of Transcranial Doppler Signals Using Artificial Neural Network
Journal of Medical Systems
IEEE Computational Science & Engineering
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Combined image compression and denoising using wavelets
Image Communication
Computer Methods and Programs in Biomedicine
A SVM-based cursive character recognizer
Pattern Recognition
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Online option price forecasting by using unscented Kalman filters and support vector machines
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Dynamic classification for video stream using support vector machine
Applied Soft Computing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Combined neural network model employing wavelet coefficients for EEG signals classification
Digital Signal Processing
Expert Systems with Applications: An International Journal
Support Vectors Machine-based identification of heart valve diseases using heart sounds
Computer Methods and Programs in Biomedicine
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
A Study on Feature Analysis for Musical Instrument Classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new hybrid intelligent system for accurate detection of Parkinson's disease
Computer Methods and Programs in Biomedicine
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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.