This paper focuses on the problem of machine learning classifier choice for network
intrusion detection, taking into consideration several ensemble classifiers from the …

Where active learning with uncertainty sampling is used to generate training sets for
classification applications, it is sensible to use the same type of classifier to select the most …

Active learning has been shown to be an effective way to alleviate some of the effort
required in utilising large collections of unlabelled data for machine learning tasks without …

Active learning (AL) is used in textual classification to alleviate the cost of labelling
documents for training. An important issue in AL is the selection of a representative sample …

This paper focuses on the problem of providing security measures, anomaly detection, and
prevention to the emerging IoT environment. We have considered several different …

Training machine learning models often requires large labelled datasets, which can be both
expensive and time-consuming to obtain. Active learning aims to selectively choose which …

The task of building labelled case bases can be approached using active learning (AL), a
process which facilitates the labelling of large collections of examples with minimal manual …

This research is a step forward in improving the accuracy of detecting anomaly in a data
graph representing connectivity between people in an online social network. The proposed …

Text classification approaches are used extensively to solve real-world challenges. The
success or failure of text classification systems hangs on the datasets used to train them …

Manually labelling large collections of text data is a time-consuming, expensive, and
laborious task, but one that is necessary to support machine learning based on text datasets …