DocumentCode :
2216678
Title :
A new signal classification technique by means of Genetic Algorithms and kNN
Author :
Rivero, Daniel ; Fernandez-Blanco, Enrique ; Dorado, Julian ; Pazos, Alejandro
Author_Institution :
Dept. of Inf. & Commun. Technol., Univ. of A Coruna, Coruna, Spain
fYear :
2011
fDate :
5-8 June 2011
Firstpage :
581
Lastpage :
586
Abstract :
Signal classification is based on the extraction of several features that will be used as inputs of a classifier. The selection of these features is one of the most crucial parts, because they will design the search space, and, therefore, will determine the difficulty of the classification. Usually, these features are selected by using some prior knowledge about the signals, but there is no method that can determine that they are the most appropriate to solve the problem. This paper proposes a new technique for signal classification in which a Genetic Algorithm is used in order to automatically select the best feature set for signal classification, in combination with a kNN as classifier system. This method was used in a well known problem and its results improve those already published in other works.
Keywords :
electroencephalography; feature extraction; genetic algorithms; medical signal processing; signal classification; electroencephalogram; epileptic signal classification technique; feature extraction; feature selection; genetic algorithms; kNN classifier system; search space; Artificial neural networks; Classification algorithms; Electroencephalography; Feature extraction; Pattern classification; Time frequency analysis; Training; Genetic Algorithms; epileptic signal classification; feature extraction; k-Nearest Neighbor; signal classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2011 IEEE Congress on
Conference_Location :
New Orleans, LA
ISSN :
Pending
Print_ISBN :
978-1-4244-7834-7
Type :
conf
DOI :
10.1109/CEC.2011.5949671
Filename :
5949671
Link To Document :
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