Background Clinical diagnosis based on machine learning usually uses case samples as
training samples, and uses machine learning to construct disease prediction models …

High dimensional and small samples characterize gene expression data and contain a large
number of genes unrelated to disease. Feature selection improves the efficiency of disease …

Differential evolution (DE) algorithms tend to be limited to local optimization when solving
complex optimization problems. Different iteration schemes lead to different convergence …

Feature selection is a critical task for optimizing system performance and reducing
computational overhead in the context of Internet of Things (IoT) applications. This paper …

Recently, when handling high-dimensional data, it has become extremely difficult to search
this optimal subset of selected features due to the restriction of reducing the exponential …

Abstract The discounted {0-1} knapsack problem (DKP) is an NP-hard problem that is more
challenging than the classical knapsack problem. In this paper, an enhanced version of the …

Neuropeptides are biomolecules with crucial physiological functions. Accurate identification
of neuropeptides is essential for understanding nervous system regulatory mechanisms …

Many feature extraction methods based on subspace learning have been proposed and
applied with good performance. Most existing methods fail to achieve a balance between …

With the widespread application of high-throughput sequencing technology in recent years,
the scale of high-dimensional gene sequence datasets has rapidly expanded. However, due …