Feature selection in Haptic-based handwritten signatures using rough sets

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DOIResolve DOI: http://doi.org/10.1109/FUZZY.2010.5584258
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Proceedings title2010 IEEE International Conference on Fuzzy Systems (FUZZ)
Conference2010 IEEE International Conference on Fuzzy Systems (FUZZ), July 18-23, 2010, Barcelona, Spain
Pages# of pages: 8
SubjectInformation and Communications Technologies
AbstractThis paper explores the use of rough set theory for feature selection in high dimensional haptic-based handwritten signatures (exploited for user identification). Two rough setbased methods for feature selection are analyzed, the first is a greedy approach while the second relies on genetic algorithms to find minimal subsets of attributes. Also, to further reduce the haptic feature space while maximizing user identification accuracy, a method is proposed where feature vectors are subsampled prior to the feature selection procedure. Rough setgenerated minimal subsets are initially exploited to determine the importance of different haptic data types (e.g. force, position, torque and orientation) in discriminating between different users. In addition, a comparison between rough setbased methods and classical machine learning techniques in the selection of minimal information-preserving subsets of featuresin high dimensional haptic datasets, is provided. The criteria for comparison are the length of the selected subsets of features and their corresponding discrimination power. Support Vector Machine classifiers are used to evaluate the accuracy of the selected minimal feature vectors. The results demonstrated that the combination of rough set and genetic algorithm techniques can outperform well-established machine learning methods in the selection of minimal subsets of features present in hapticbased handwritten signatures.
Publication date
AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology
Peer reviewedYes
NPARC number15336783
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Record identifier1f02918c-429c-4863-96f4-c4faeaf7ab30
Record created2010-06-10
Record modified2016-05-09
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