Feature selection (FS) is a major issue in developing efficient pattern recognition systems.
FS refers to the selection of the most appropriate subset of features that describes …

The rapid advance of computer based high-throughput technique have provided
unparalleled opportunities for humans to expand capabilities in production, services …

Feature selection goal is to get rid of redundant and irrelevant features. The problem of
feature subset selection is that of finding a subset of the original features of a dataset, such …

Feature selection is the task of selecting a small subset from original features that can
achieve maximum classification accuracy. This subset of features has some very important …

Feature selection is one of the most important preprocessing steps for a data mining, pattern
recognition or machine learning problem. Finding an optimal subset of features, among all …

This paper proposes a feature selection method that combines various feature selection
techniques. Feature selection has been realized as one of the most important processes in …

Feature selection algorithms are the cornerstone of machine learning. By increasing the
properties of the samples and samples, the feature selection algorithm selects the significant …

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 …

Feature subset selection is basically an optimization problem for choosing the most
important features from various alternatives in order to facilitate classification or mining …

The task of choosing the appropriate classifier for a given scenario is not an easy-to-solve
question. First, there is an increasingly high number of algorithms available belonging to …