Feature selection is one of the key problems for machine learning and data mining. In this
review paper, a brief historical background of the field is given, followed by a selection of …

In the era of big data, the dimensionality of data is increasing dramatically in many domains.
To deal with high dimensionality, online feature selection becomes critical in big data …

Co-saliency is used to discover the common saliency on the multiple images, which is a
relatively underexplored area. In this paper, we introduce a new cluster-based algorithm for …

This paper provides an alternative method for creating an optimal subset from features
which in turn represent the whole features through improving the moth-flame optimization …

In many image processing and pattern recognition problems, visual contents of images are
currently described by high-dimensional features, which are often redundant and noisy …

The high dimensionality of data hinders the learning ability of machine learning algorithms.
Feature selection techniques can be used to reduce dimensionality, which is an important …

Adopting effective model to access the desired images is essential nowadays with the
presence of a huge amount of digital images. The present paper introduces an accurate and …

Feature selection is beneficial for improving the performance of general machine learning
tasks by extracting an informative subset from the high-dimensional features. Conventional …

Selection of feature subset is a preprocessing step in computational learning, and it serves
several purposes like reducing the dimensionality of a dataset, decreasing the …

Content-based image retrieval (CBIR), which is aimed to search images from a large size
image database based on visual contents of images in an efficient and accurate way as per …