Identity verification based on haptic handwritten Signature: Novel fitness functions for GP framework

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Alsulaiman:2013:HAVE,

• author = "Fawaz A. Alsulaiman and Julio J. Valdes and Abdulmotaleb {El Saddik}",
• booktitle = "IEEE International Symposium on Haptic Audio Visual Environments and Games (HAVE 2013)",
• title = "Identity verification based on haptic handwritten Signature: Novel fitness functions for GP framework",
• year = "2013",
• month = oct,
• pages = "98--102",
• keywords = "genetic algorithms, genetic programming, handwriting recognition, haptic interfaces, GP framework, evolutionary processes, false rejection rate, haptic based handwritten signatures, identity verification, novel fitness functions, Accuracy, Educational institutions, Evolutionary computation, Gene expression, Haptic interfaces, Programming",
• DOI = "doi:10.1109/HAVE.2013.6679618",
• size = "5 pages",
• abstract = "Fitness 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.",
• notes = "Also known as \cite{6679618}",

}

Genetic Programming entries for Fawaz A Alsulaiman Julio J Valdes Abdulmotaleb El Saddik

Citations