Identity verification based on haptic handwritten signature : novel fitness functions for GP framework

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Conference2013 IEEE International Symposium on Haptic Audio Visual Environments and Games (HAVE 2013), Oct 26-27, 2013, Istanbul, Turkey
Pages98102; # of pages: 5
AbstractFitness functions are the evaluation measures driving evolutionary processes towards solutions. In this paper, three fitness functions are proposed for solving the unbalanced dataset problem in Haptic-based handwritten signatures using genetic programming (GP). The use of these specifically designed fitness functions produced simpler analytical expressions than those obtained with currently available fitness measures, while keeping comparable classification accuracy. The functions introduced in this paper capture explicitly the nature of unbalanced data, exhibit better dimensionality reduction and have better False Rejection Rate.
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AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number21270500
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Record identifier71ef8fa7-ac7f-4144-b873-1ae61c9c3d5b
Record created2014-02-14
Record modified2016-05-09
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