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

Authors:
Fawaz A. Alsulaiman;Nizar Sakr;Julio J. Valdés;Abdulmotaleb El Saddik;Nicolas D. Georganas
Affiliations:
School of Information Technology and Engineering, University of Ottawa, Canada;School of Information Technology and Engineering, University of Ottawa, Canada;National Research Council Canada, Institute for Information Technology, Ottawa, Canada;School of Information Technology and Engineering, University of Ottawa, Canada;-
Venue:
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Year:
2009

Citing 5
Cited 1

Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)

Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)
Experimental perspectives on learning from imbalanced data

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The class imbalance problem: A systematic study

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Identity verification based on handwritten signatures with haptic information using genetic programming

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)

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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 undersampled (balanced) version of the datasets. The results demonstrated that for all datasets, whether imbalanced or undersampled, 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.