Despite significant successes achieved in knowledge discovery, traditional machine
learning methods may fail to obtain satisfactory performances when dealing with complex …

This paper surveys main principles of feature selection and their recent applications in big
data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and …

Ensemble learning for anomaly detection has been barely studied, due to difficulty in
acquiring ground truth and the lack of inherent objective functions. In contrast, ensemble …

Cluster ensemble is one of the main branches in the ensemble learning area which is an
important research focus in recent years. The objective of cluster ensemble is to combine …

Traditional random subspace-based classifier ensemble approaches (RSCE) have several
limitations, such as viewing the same importance for the base classifiers trained in different …

Ensemble classifier refers to a group of individual classifiers that are cooperatively trained
on data set in a supervised classification problem. In this paper we present a review of …

There is significant interest in the network management and industrial security community
about the need to identify the “best” and most relevant features for network traffic in order to …

Online portals provide an enormous amount of news articles every day. Over the years,
numerous studies have concluded that news events have a significant impact on forecasting …

Reliable and efficient power modeling from meteorological wind data is vital for optimal
implementation and monitoring of wind energy, and it is important for understanding turbine …

Increasing student retention has been a common goal of many academic institutions,
especially in the university level. The negative effects of student attrition are evident to …