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CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
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IEEE Spectrum
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In this article, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. A comparison of GP-based classification with classical classifiers including support vector machine, k-nearest neighbors, naïve Bayes, and random forest is conducted. In addition, the use of GP in discovering small knowledge-preserving subsets of features in high-dimensional datasets of haptic-based signatures is investigated and several approaches are explored. Subsets of features extracted from GP-generated models (analytic functions) are also exploited to determine the importance and relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification. The results revealed that GP classifiers compare favorably with the classical methods and use a much fewer number of attributes (with simple function sets).