In the era of big data, machine learning has emerged as an effective technique in many real-
world applications including medical diagnosis and data-driven decision making. Feature …

In applications of algorithms, feature selection has got much attention of researchers, due to
its ability to overcome the curse of dimensionality, reduce computational costs, increase the …

Feature selection is usually a separate procedure which can not benefit from result of the
data exploration. In this paper, we propose a unsupervised feature selection method which …

Feature selection is used to improve the efficiency of learning algorithms by finding an
optimal subset of features. However, most feature selection techniques can handle only …

Classification is found to be an important field of research for many applications such as
medical diagnosis, credit risk and fraud analysis, customer segregation, and business …

Feature selection can help to reduce data redundancy and improve algorithm performance
in actual tasks. Most of the embedded feature selection models are constructed based on …

The traditional feature selection algorithms do not consider the nonlinear relationships
between features because their objective functions are linear. We propose a feature …

Abstract Information overload has raggedly increased as a result of the advances in the
aspect of storage capabilities and data collection in previous years. The growth seen in the …

An essential step in data mining and machine learning is selecting a useful feature subset
from the high-dimensional feature space. Many existing feature selection algorithms only …