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Optimal feature subset selection for neuron spike sorting using the genetic algorithm

Khan, Burhan, Bhatti, Asim, Johnstone, Michael, Hanoun, Samer, Creighton, Douglas and Nahavandi, Saeid 2015, Optimal feature subset selection for neuron spike sorting using the genetic algorithm, in ICONIP 2015 : Neural information processing : 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015 : proceedings, Spring, New York, N.Y., pp. 364-370, doi: 10.1007/978-3-319-26535-3_42.

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Title Optimal feature subset selection for neuron spike sorting using the genetic algorithm
Author(s) Khan, Burhan
Bhatti, AsimORCID iD for Bhatti, Asim orcid.org/0000-0001-6876-1437
Johnstone, MichaelORCID iD for Johnstone, Michael orcid.org/0000-0002-3005-8911
Hanoun, SamerORCID iD for Hanoun, Samer orcid.org/0000-0002-8697-1515
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Conference name Neural Information Processing. Conference (22nd : 2015 : Istanbul, Turkey)
Conference location Istanbul, Turkey
Conference dates 9-12 Nov. 2015
Title of proceedings ICONIP 2015 : Neural information processing : 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015 : proceedings
Publication date 2015
Series Lecture Notes in Computer Science, v.9490
Start page 364
End page 370
Total pages 7
Publisher Spring
Place of publication New York, N.Y.
Keyword(s) Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Computer Science
Genetic algorithm
Super-Paramagnetic clustering
Neuron spike sorting
Features selection
Optimization
INFORMATION
Summary It 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.
ISBN 9783319265346
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-26535-3_42
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
080109 Pattern Recognition and Data Mining
08 Information And Computing Sciences
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30080687

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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