| Download | - View accepted manuscript: Feature selection in Haptic-based handwritten signatures using rough sets (PDF, 612 KiB)
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| DOI | Resolve DOI: https://doi.org/10.1109/FUZZY.2010.5584258 |
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| Author | Search for: Sakr, Nizar; Search for: Alsulaiman, Fawaz A.; Search for: Valdes, Julio J.1; Search for: El Saddik, Abdulmotaleb; Search for: Georganas, Nicolas D. |
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| Affiliation | - National Research Council of Canada. NRC Institute for Information Technology
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| Format | Text, Article |
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| Conference | 2010 IEEE International Conference on Fuzzy Systems (FUZZ), July 18-23, 2010, Barcelona, Spain |
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| Subject | Information and Communications Technologies |
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| Abstract | This 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. |
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| Publication date | 2010-07-23 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 15336783 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 1f02918c-429c-4863-96f4-c4faeaf7ab30 |
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| Record created | 2010-06-10 |
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| Record modified | 2020-04-17 |
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