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Optimal feature subset selection for neuron spike sorting using the genetic algorithm
conference contribution
posted on 2015-01-01, 00:00 authored by B Khan, Asim BhattiAsim Bhatti, Michael JohnstoneMichael Johnstone, Samer HanounSamer Hanoun, Douglas CreightonDouglas Creighton, Saeid NahavandiIt is crucial for a neuron spike sorting algorithm to cluster data from different neurons efficiently. In this study, the search capability of the Genetic Algorithm (GA) is exploited for identifying the optimal feature subset for neuron spike sorting with a clustering algorithm. Two important objectives of the optimization process are considered: to reduce the number of features and increase the clustering performance. Specifically, we employ a binary GA with the silhouette evaluation criterion as the fitness function for neuron spike sorting using the Super-Paramagnetic Clustering (SPC) algorithm. The clustering results of SPC with and without the GA-based feature selector are evaluated using benchmark synthetic neuron spike data sets. The outcome indicates the usefulness of the GA in identifying a smaller feature set with improved clustering performance.
History
Event
Neural Information Processing. Conference (22nd : 2015 : Istanbul, Turkey)Volume
9490Series
Lecture Notes in Computer SciencePagination
364 - 370Publisher
SpringerLocation
Istanbul, TurkeyPlace of publication
New York, N.Y.Publisher DOI
Start date
2015-11-09End date
2015-11-12ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319265346Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, SpringerTitle of proceedings
ICONIP 2015 : Neural information processing : 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015 : proceedingsUsage metrics
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