Selecting relevant feature subsets is vital in machine learning, and multiclass feature
selection is harder to perform since most classifications are binary. The feature selection …

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 is a significant task in data mining and machine learning applications
which eliminates irrelevant and redundant features and improves learning performance. In …

This survey aims at providing a state-of-the-art overview of feature selection and fusion
strategies, which select and combine multi-view features effectively to accomplish …

Plenty of feature selection methods are available in literature due to the availability of data
with hundreds of variables leading to data with very high dimension. Feature selection …

Feature selection, as a dimensionality reduction technique, aims to choosing a small subset
of the relevant features from the original features by removing irrelevant, redundant or noisy …

Nowadays, the growth of the high-throughput technologies has resulted in exponential
growth in the harvested data with respect to both dimensionality and sample size. The trend …

Video semantic recognition usually suffers from the curse of dimensionality and the absence
of enough high-quality labeled instances, thus semisupervised feature selection gains …

Most objects and data in the real world are of multiple types, interconnected, forming
complex, heterogeneous but often semi-structured information networks. However, most …

Pavement condition information is a significant component in Pavement Management
Systems. The labeling and quantification of the type, severity, and extent of surface cracking …