出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Anomaly detection is vital for automated data analysis, with specific applications spanning almost
every domain. In this paper, we propose a hybrid supervised learning of anomaly detection using
frequent itemset mining and random forest with an ensemble probabilistic voting method, which
outperforms the alternative supervised learning methods through the commonly used measures for
anomaly detection: accuracy, true positive rate (i.e. recall) and false positive rate. To justify our
claim, a benchmark dataset is used to evaluate the efficiency of the proposed approach, where the
results illustrate its benefits.
