%0 Conference Paper
%A Jian, Z.
%A Sakai, H.
%A Watada, J.
%A Roy, A.
%A Hassan, M.H.B.
%D 2019
%F scholars:11006
%I Institute of Electrical and Electronics Engineers Inc.
%K Learning algorithms; Statistical tests, Apriori algorithms; Cross validation; Event recognition; Logical properties; Machine learning models; Missing values; Rule-based models; Training data sets, Big data
%P 5888-5896
%R 10.1109/BigData47090.2019.9006420
%T An Apriori-based Data Analysis on Suspicious Network Event Recognition
%U https://khub.utp.edu.my/scholars/11006/
%X Apriori-based rule generators, which are powered by the DIS-Apriori algorithm and the NIS-Apriori algorithm, are applied to analyze the data sets available in the IEEE BigData 2019 Cup: Suspicious Network Event Recognition. Then, each missing value in the test data set is decided by using the obtained rules. The advantage of our rule-based model is that the obtained rules are very easy to understand in comparison with other 'black-box' machine learning models. Furthermore, two algorithms preserve the logical property 'completeness,' so they generate rules without excess and deficiency. In evaluation, the AUC measure seems unfavorable to our model, so we employed 3-fold cross-validation for the training data set, and we obtained a 94 mean score. This result ensures the validity of our model. We report several meaningful results in this experiment, as well as the estimation of missing values. © 2019 IEEE.
%Z cited By 3; Conference of 2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference Date: 9 December 2019 Through 12 December 2019; Conference Code:157991