One way to improve accuracy of a classifier is to use the minimum number of features. Many
feature selection techniques are proposed to find out the most important features. In this …

This article investigates the performance of combining support vector machines (SVM) and
various feature selection strategies. Some of them are filter-type approaches: general …

This paper presents a novel feature selection approach to deal with issues of high
dimensionality in biomedical data classification. Extensive research has been performed in …

Feature selection is an important pre-processing step, which can reduce the dimensionality
of a dataset and increase the accuracy and efficiency of a learning/classification algorithm …

Feature selection methods are used to find the set of features that yield the best
classification accuracy for a given data set. This results in lower training and classification …

Wrapper feature selection methods are widely used to select relevant features. However,
wrappers only use a single classifier. The downside to this approach is that each classifier …

In the context of optimal subset selection, hybrid feature selection approaches play a
significant role that has been the topic of a substantial number of studies because of the …

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 …

Background and objective Medical data plays a decisive role in disease diagnosis. The
classification accuracy of high-dimensional datasets is often diminished by several …

The growing abundance of information necessitates the need for appropriate methods for
organization and evaluation. Mining data for information and extracting conclusions has …