Feature selection and classification in genetic programming: Application to haptic-based biometric data

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

@InProceedings{Alsulaiman:2009:ieeeCISDA,

• author = "Fawaz A. Alsulaiman and Nizar Sakr and Julio J. Valdes and Abdulmotaleb {El Saddik} and Nicolas D. Georganas",
• title = "Feature selection and classification in genetic programming: Application to haptic-based biometric data",
• booktitle = "IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2009",
• year = "2009",
• month = jul,
• pages = "1--7",
• keywords = "genetic algorithms, genetic programming, gene expression programming, analytic function, dimensionality reducers, feature selection, haptic dataset, haptic-based biometric data, haptic-based biometrics problem, high-dimensional haptic feature space, perfect classification model, feature extraction, haptic interfaces, pattern classification",
• DOI = "doi:10.1109/CISDA.2009.5356540",
• abstract = "In this paper, a study is conducted in order to explore the use of genetic programming, in particular gene expression programming (GEP), in finding analytic functions that can behave as classifiers in high-dimensional haptic feature spaces. More importantly, the determined explicit functions are used in discovering minimal knowledge-preserving subsets of features from very high dimensional haptic datasets, thus acting as general dimensionality reducers. This approach is applied to the haptic-based biometrics problem; namely, in user identity verification. GEP models are initially generated using the original haptic biometric datatset, which is imbalanced in terms of the number of representative instances of each class. This procedure was repeated while considering an under-sampled (balanced) version of the datasets. The results demonstrated that for all datasets, whether imbalanced or under-sampled, a certain number (on average) of perfect classification models were determined. In addition, using GEP, great feature reduction was achieved as the generated analytic functions (classifiers) exploited only a small fraction of the available features.",
• notes = "Also known as \cite{5356540}",

}

Genetic Programming entries for Fawaz A Alsulaiman Nizar Sakr Julio J Valdes Abdulmotaleb El Saddik Nicolas D Georganas

Citations