Abstract Machine Learning (ML) has been enjoying an unprecedented surge in applications
that solve problems and enable automation in diverse domains. Primarily, this is due to the …

Abstract Multiple Classifier Systems (MCS) have been widely studied as an alternative for
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …

Data fusion is a prevalent way to deal with imperfect raw data for capturing reliable, valuable
and accurate information. Comparing with a range of classical probabilistic data fusion …

Intrusion detection systems (IDSs) play a pivotal role in computer security by discovering
and repealing malicious activities in computer networks. Anomaly-based IDS, in particular …

Network anomaly detection is an important and dynamic research area. Many network
intrusion detection methods and systems (NIDS) have been proposed in the literature. In this …

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach,
Ensemble Methods: Foundations and Algorithms shows how these accurate methods are …

The aim of an intrusion detection systems (IDS) is to detect various types of malicious
network traffic and computer usage, which cannot be detected by a conventional firewall …

机器学习的求解过程可以看作是在假设空间中搜索一个具有强泛化能力和高鲁棒性的学习模型,
而在假设空间中寻找合适模型的过程是较为困难的. 然而, 集成学习作为一类组合优化的学习 …

Detection of cyber attacks against vehicles is of growing interest. As vehicles typically afford
limited processing resources, proposed solutions are rule-based or lightweight machine …

A current focus of intense research in pattern classification is the combination of several
classifier systems, which can be built following either the same or different models and/or …