**3. AML selection strategies**

There are a number of AML query selection strategies, which have been presented by Settles [5]: (1) Uncertainty sampling is the simplest and the most commonly used strategy. Uncertainty sampling focuses on selecting the instance that the classifier is most uncertain about to label. This strategy can be divided into two categories: maximum entropy of the estimated label and minimum margin (distance of an instance to the decision boundary). (2) Expected error reduction, which aims to query instance that minimizes the expected error of the classifier. (3) Query by Committee (QBC) in which the most informative instance is the one that a committee of classifiers finds most disagreement. Bagging and boosting are used to generate committees of classifiers from the same dataset. They aim to combine a set of weak classifiers to create a single strong classifier. While bagging creates each base classifier independently, boosting allows these classifiers to influence each other during training process [18]. Boosting is an iterative process that initially assigns equal weight to each of the training samples; then the weights are modified based on the error rate of individual classifier.
