Comparing Structure Learning Algorithms of
نویسندگان
چکیده
In this paper, we empirically evaluate effectiveness of structure learning of Bayesian Network when applying such networks to the domain of Keystroke Dynamics authentication. We compare four structure learning methods of Bayesian Network Classifier – Genetic, TAN, K2, and Hill Climbing algorithms, on our authentication model, namely Classify User via Short-text and IP Model (CUSIM). The results show that Genetic algorithm was best suited to our model. The findings from the study also indicate that the Accuracy, FAR, and FRR rate of Genetic algorithm are better than other algorithms tested in this work. Moreover, we found that TAN algorithm gives better results in some scenario than other algorithms.
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