Simultaneous ranking and selection of keystroke dynamics features through a novel multi-objective binary bat algorithm

In this paper, we propose a novel multi-objective binary bat algorithm for simultaneous ranking and selection of keystroke dynamics features. The proposed algorithm uses the V shaped binarization function. Simulation results show that, the proposed algorithm can efficiently identify the most important features of the data set. Of the three feature classes, the key down hold time features (H-features) are proofed to be the most dominant features. Using H-features only in classification decreases the mean square error (MSE) by 2% compared to choosing all features in classification. The UD features are the second ranked features. The worst features are the DD features which represent the largest MSE when being used individually in the classification process. The results are performed using two classifiers for comparisons; the linear and the quadratic classifiers. The quadratic classifier outperforms the linear classifier with respect to the mean square error (MSE) and the average number of features selected.