Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Journal of Intelligent Information Systems
Fuzzy least squares support vector machines for multiclass problems
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
A hybrid genetic algorithm for feature selection wrapper based on mutual information
Pattern Recognition Letters
On maximum mutual information speaker-adapted training
Computer Speech and Language
Mean frequency derived via Hilbert-Huang transform with application to fatigue EMG signal analysis
Computer Methods and Programs in Biomedicine
Feature selection in MLPs and SVMs based on maximum output information
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
An SVM-based machine learning method for accurate internet traffic classification
Information Systems Frontiers
Computers in Biology and Medicine
Functional activity maps based on significance measures and Independent Component Analysis
Computer Methods and Programs in Biomedicine
Engineering Applications of Artificial Intelligence
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This paper presents an effective mutual information-based feature selection approach for EMG-based motion classification task. The wavelet packet transform (WPT) is exploited to decompose the four-class motion EMG signals to the successive and non-overlapped sub-bands. The energy characteristic of each sub-band is adopted to construct the initial full feature set. For reducing the computation complexity, mutual information (MI) theory is utilized to get the reduction feature set without compromising classification accuracy. Compared with the extensively used feature reduction methods such as principal component analysis (PCA), sequential forward selection (SFS) and backward elimination (BE) etc., the comparison experiments demonstrate its superiority in terms of time-consuming and classification accuracy. The proposed strategy of feature extraction and reduction is a kind of filter-based algorithms which is independent of the classifier design. Considering the classification performance will vary with the different classifiers, we make the comparison between the fuzzy least squares support vector machines (LS-SVMs) and the conventional widely used neural network classifier. In the further study, our experiments prove that the combination of MI-based feature selection and SVM techniques outperforms other commonly used combination, for example, the PCA and NN. The experiment results show that the diverse motions can be identified with high accuracy by the combination of MI-based feature selection and SVM techniques. Compared with the combination of PCA-based feature selection and the classical Neural Network classifier, superior performance of the proposed classification scheme illustrates the potential of the SVM techniques combined with WPT and MI in EMG motion classification.