Guided Lamb waves are highly valued in structural health monitoring for identifying critical
damage. The essence of Lamb wave-based damage detection is finding and processing …

Multi-label feature selection, which addresses the challenge of high dimensionality in multi-
label learning, has wide applicability in pattern recognition, machine learning, and related …

Network assaults pose significant security concerns to network services; hence, new
technical solutions must be used to enhance the efficacy of intrusion detection systems …

Feature selection is a significant preprocessing technique for data mining, which can
promote the accuracy of data classification and shrink feature space by eliminating …

Feature selection has been studied by many researchers using information theory to select
the most informative features. Up to now, however, little attention has been paid to the …

The neighborhood rough set theory is a helpful instrument for working with data that is
numerical, and the performance of its uncertainty measures is generally stable. Even one …

Feature selection has been recognized for long as an important preprocessing technique to
reduce dimensionality and improve the performance of regression and classification tasks …

As a fundamental granular computing strategy, neighborhood granulation has been
acknowledged as an intuitive and effective approach to feature evaluation and selection …

Most extant feature selection works neglect interactive features in the form of groups, leading
to the omission of some important discriminative information. Moreover, the prevalence of …

Attribute reduction is an important application of rough set theory. Most existing rough set
models do not consider the weight information of attributes in information systems. In this …