Abstract
Active learning aims to train an accurate model with minimum cost by labeling the most informative instances without compromising the model performance. So, choosing an efficient criterion for instance selection is the most important step. Sampling stage is the main issue in active learning for many problems such as intrusion detection system. There are many methods for sampling stage to select the informative instances, but what the method should be used to provide the most accurate to the Intrusion Detection System (IDS). So, we made a comparison between three of these methods, uncertainty sampling, Query By Committee (QBC) and expected model change. The contribution of this study is analyzing and examining three of common strategies that used to select the most informative instances to determine the best one of them. The experimental result showed that the expected model change method achieved the highest accuracy compared with uncertainty sampling and query by committee methods.
Highlights
Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed
The performance of the ID models based on the sampling methods that use in active learning was compared
The performance of the active learning procedure based on three sampling approaches for six different initial samples with size of 100 is show in Fig 8 to 13, the points represent the three sampling methods Query By Committee (QBC), uncertainty sampling and expected model change
Summary
Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. The correct outputs (targets) are known and are given to the model during the learning process (Salah et al, 2011; Qatawneh et al, 20017; Farhan et al, 2015) This type of learning is usually fast and accurate, while this approach is not applied in active learning. Large of sensitive institutions require large amounts of labeled data to obtain an accurate model such as network intrusion detection system. To solve such of these problems active learning is used. The principle of work for this framework, the learner has the freedom and influence to select which instances will be added to its training set (Roy and McCallum, 2001; Cohn et al, 1994)
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