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Articles by Choo-Yee Ting
Total Records ( 2 ) for Choo-Yee Ting
  Choo-Yee Ting and Somnuk Phon-Amnuaisuk
  Log data provide valuable insight into observable behavioural patterns, which could be inferred to study a learner’s cognitive processes, levels of motivation and levels of knowledge acquisition. To date, most of the research work has been devoted to study the different methods to analyze and interpret log data. Little attention, however, has been given to use log data as a tool to investigate the behaviour of Bayesian learner models. In this light, this article discusses how log data could be employed to investigate the performance of Bayesian learner models. The log data were firstly transformed into a set of structured dataset, which conformed to the INQPRO’s learner model. The transformed dataset were then fed into different versions of INQPRO’s learner model to obtain their predictive accuracies. From the predictive accuracies, an optimal learner model was identified. Empirical results indicated that the log data approach provides an efficient way to study the behaviour of a Bayesian learner model
  Kok-Chin Khor , Choo-Yee Ting and Somnuk-Phon Amnuaisuk
  Problem statement: Implementing a single or multiple classifiers that involve a Bayesian Network (BN) is a rising research interest in network intrusion detection domain. Approach: However, little attention has been given to evaluate the performance of BN classifiers before they could be implemented in a real system. In this research, we proposed a novel approach to select important features by utilizing two selected feature selection algorithms utilizing filter approach. Results: The selected features were further validated by domain experts where extra features were added into the final proposed feature set. We then constructed three types of BN namely, Naive Bayes Classifiers (NBC), Learned BN and Expert-elicited BN by utilizing a standard network intrusion dataset. The performance of each classifier was recorded. We found that there was no difference in overall performance of the BNs and therefore, concluded that the BNs performed equivalently well in detecting network attacks. Conclusion/Recommendations: The results of the study indicated that the BN built using the proposed feature set has less features but the performance was comparable to BNs built using other feature sets generated by the two algorithms.
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