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ROBIO
2006
IEEE

GA-based Feature Subset Selection for Myoelectric Classification

14 years 5 months ago
GA-based Feature Subset Selection for Myoelectric Classification
– This paper presents an ongoing investigation to select optimal subset of features from set of well-known myoelectric signals (MES) features in time and frequency domains. Four channel of myoelectric signal from upper limb muscles are used in this paper to classify six distinctive activities. Cascaded genetic algorithm (GA) has been adopted as the search strategy in feature subset selection. Davies–Bouldin index (DBI) and Fishers linear discriminant index (FLDI) are employed as the filter objective functions and linear discriminant analysis (LDA) has been used as the wrapper objective function. Results prove more accurate and reliable classification for the elite subset of features applying to artificial neural networks as the classifier.
Mohammadreza Asghari Oskoei, Huosheng Hu
Added 12 Jun 2010
Updated 12 Jun 2010
Type Conference
Year 2006
Where ROBIO
Authors Mohammadreza Asghari Oskoei, Huosheng Hu
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