Feature selection aims to select a feature subset from an original feature set based on a
certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it …

Network intrusion is a growing threat with potentially severe impacts, which can be
damaging in multiple ways to network infrastructures and digital/intellectual assets in the …

Feature selection is viewed as an important preprocessing step for pattern recognition,
machine learning, and data mining. Neighborhood is one of the most important concepts in …

Many real data increase dynamically in size. This phenomenon occurs in several fields
including economics, population studies, and medical research. As an effective and efficient …

Feature selection problem in data mining is addressed here by proposing a bi-objective
genetic algorithm based feature selection method. Boundary region analysis of rough set …

With the advent of the age of big data, a typical big data set called limited labeled big data
appears. It includes a small amount of labeled data and a large amount of unlabeled data …

Many search strategies have been exploited for the task of feature selection (FS), in an effort
to identify more compact and better quality subsets. Such work typically involves the use of …

Abstract Background and Objective Retrieving meaningful information from high
dimensional dataset is an important and challenging task. Normally, medical dataset suffers …

Feature selection is one of the trustworthy processes of dimensionality reduction technique
to select a subset of relevant and non-redundant features from large datasets. Ensemble …

Rough set theory, as one of the most useful soft computing methods dealing with vague and
uncertain information, has been successfully applied to many fields, and one of its main …