Data preprocessing and reduction have become essential techniques in current knowledge
discovery scenarios, dominated by increasingly large datasets. These methods aim at …

Real-world problems are commonly characterized by a high feature dimensionality, which
hinders the modelling and descriptive analysis of the data. However, some of these data …

Many evolutionary computation (EC) methods have been used to solve feature selection
problems and they perform well on most small-scale feature selection problems. However …

Classifying videos according to content semantics is an important problem with a wide range
of applications. In this paper, we propose a hybrid deep learning framework for video …

The network traffic in the intrusion detection system (IDS) has unpredictable behaviour due
to the high computational power. The complexity of the system increases; thus, it is required …

The “curse of dimensionality” brings new challenges to the feature selection (FS) problem,
especially in bioinformatics filed. In this paper, we propose a hybrid Two-Stage Teaching …

Dimensionality reduction is an important and challenging task in machine learning and data
mining. It can facilitate data clustering, classification and information retrieval. As an efficient …

In this paper, a factor graph model for unsupervised feature selection (FGUFS) is proposed.
FGUFS explicitly measures the similarities between features; these similarities are passed to …

Sparse Bayesian learning is a state-of-the-art supervised learning algorithm that can choose
a subset of relevant samples from the input data and make reliable probabilistic predictions …

Massive data streams are continuously being generated from sources such as social media,
broadcast news, etc., and typically these datapoints lie in high-dimensional spaces (such as …