Feature selection (FS), as one of the most significant preprocessing techniques in the fields
of machine learning and pattern recognition, has received great attention. In recent years …

Enterprise credit risk prediction in the supply chain context is an important step for decision
making and early credit crisis warnings. Improving the prediction performance of this task is …

Feature selection is a crucial preprocessing step in the sphere of machine learning and data
mining, devoted to reducing the data dimensionality to improve the performance of learning …

To reduce the dimensionality of high-dimensional data and enhance its classification
accuracy, feature selection can be regarded as a multiobjective optimization problem that …

Glaucoma is the second most common cause of vision loss. Manual screening of a patient's
eye or screening through a fundus image of the patient's eye requires expert …

Glaucoma, commonly known as the silent thief of sight, is the second most common cause of
blindness in humans, and the number of cases is steadily increasing. Conventional …

In a canonical particle swarm optimization (PSO) algorithm, the fitness is a widely accepted
criterion when selecting exemplars for a particle, which exhibits promising performance in …

Feature selection (FS) is crucial to transforming high-dimensional data into low-dimensional
data. The FS approach selects influential traits and ignores the rest. This approach improves …

Swarm intelligence algorithms can efficiently solve feature selection optimization problems
for classification, and their classification performance is also excellent. The Sparrow Search …

Gene selection is a process of selecting discriminative genes from microarray data that
helps to diagnose and classify cancer samples effectively. Swarm intelligence evolution …