Feature selection methods are deployed in machine-learning algorithms for reducing the
redundancy in the dataset and to increase the clarity in the system models without loss of …

Feature selection plays an important role in pattern classification. Its purpose is to remove
redundant features from data set as many as possible. The presence of useless features …

Feature selection plays an important role in pattern classification. In this paper, a hybrid
genetic algorithm (HGA) is adopted to find a subset of the most relevant features. The …

One of the challenges in data mining is the dimensionality of data, which is often very high
and prevalent in many domains, such as text categorization and bio-informatics. The high …

Feature Selection (FS) is an imperative issue in data mining and machine learning. It is an
inevitable task to shorter the number of features presented in the initial data set for better …

Feature selection is used for finding a feature subset that has the most discriminative
information from the original feature set. In practice, since we do not know the classifier to be …

With the rapid development of information techniques, the dimensionality of data in many
application domains, such as text categorization and bioinformatics, is getting higher and …

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 …

In the task of pattern classification features play a very important role. Hence, the selection of
suitable features is necessary as most of the raw data might be redundant or irrelevant to the …