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. Attached Files Name Description MIMEType Size Downloads Title Optimal feature subset selection for neuron spike sorting using the genetic algorithm Author(s) Khan, Burhan Bhatti, Asim orcid.org/0000-0001-6876-1437 Johnstone, Michael orcid.org/0000-0002-3005-8911 Hanoun, Samer orcid.org/0000-0002-8697-1515 Creighton, Douglas orcid.org/0000-0002-9217-1231 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 Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 12 Jan 2016, 13:11:45 EST Wed, 13 Jan 2016, 04:00:56 EST Mon, 18 Jan 2016, 07:32:15 EST Mon, 18 Apr 2016, 12:35:35 EST Tue, 26 Apr 2016, 11:07:40 EST Wed, 27 Apr 2016, 06:07:18 EST Thu, 19 May 2016, 16:23:49 EST Filtered Full Citation counts:   Cited 4 times in TR Web of Science Cited 6 times in Scopus Search Google Scholar Access Statistics: 516 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Tue, 26 Apr 2016, 11:07:40 EST

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