Machine learning has shown utility in detecting patterns within large, unstructured, and
complex datasets. One of the promising applications of machine learning is in precision …

Nowadays, being in digital era the data generated by various applications are increasing
drastically both row-wise and column wise; this creates a bottleneck for analytics and also …

Feature selection plays a critical role in biomedical data mining, driven by increasing feature
dimensionality in target problems and growing interest in advanced but computationally …

Intrusion in wireless sensor networks (WSNs) aims to degrade or even eliminating the
capability of these networks to provide its functions. In this paper, an enhanced intrusion …

We present AMIGOS-A dataset for Multimodal research of affect, personality traits and mood
on Individuals and GrOupS. Different to other databases, we elicited affect using both short …

Feature selection (FS) methods can be used in data pre-processing to achieve efficient data
reduction. This is useful for finding accurate data models. Since exhaustive search for …

The detection and monitoring of emotions are important in various applications, eg, to
enable naturalistic and personalised human-robot interaction. Emotion detection often …

In this work, we present a review of the state of the art of information-theoretic feature
selection methods. The concepts of feature relevance, redundance, and complementarity …

Feature selection is playing an increasingly significant role with respect to many computer
vision applications spanning from object recognition to visual object tracking. However, most …

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 …