Machine Learning
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
User authentication through typing biometrics features
IEEE Transactions on Signal Processing
A machine learning approach to keystroke dynamics based user authentication
International Journal of Electronic Security and Digital Forensics
A hybrid GA-PSO fuzzy system for user identification on smart phones
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
On the use of rough sets for user authentication via keystroke dynamics
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Review Article: Biometric personal authentication using keystroke dynamics: A review
Applied Soft Computing
Combining multiple predictive models using genetic algorithms
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
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User authentication based on keystroke dynamics is concerned with accepting or rejecting someone based on the way the person types. A timing vector is composed of the keystroke duration times interleaved with the keystroke interval times. Which times or features to use in a classifier is a classic feature selection problem. Genetic algorithm based wrapper approach does not only solve the problem, but also provides a population of “fit” classifiers which can be used in ensemble. In this paper, we propose to add uniqueness term in the fitness function of genetic algorithm. Preliminary experiments show that the proposed approach performed better than two phase ensemble selection approach and prediction based diversity term approach.