A Hybrid Improved Multi-objective Particle Swarm Optimization Feature Selection Algorithm for High-Dimensional Small Sample Data

Article Biology

AltWOA: Altruistic Whale Optimization Algorithm for feature selection on microarray datasets

Rohit Kundu et al.

Summary: In this paper, a novel Altruistic Whale Optimization Algorithm (AltWOA) is proposed for feature selection problem in high-dimensional microarray data. By embedding the concept of altruism in the whale population, AltWOA achieves efficient propagation of candidate solutions and outperforms popular techniques in terms of accuracy and selected features on multiple datasets.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

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Article Computer Science, Artificial Intelligence

Iteratively local fisher score for feature selection

Min Gan et al.

Summary: In machine learning, feature selection is crucial for dimension reduction, and the Fisher score (FS) method is widely used. However, FS lacks consideration of feature association and local information. The proposed ILFS method addresses these issues by focusing on local structure and using iterative selection similar to SFS. The experimental results demonstrate the effectiveness of ILFS in classification tasks on different datasets.

APPLIED INTELLIGENCE (2021)

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Article Biology

A novel gene selection method for gene expression data for the task of cancer type classification

N. Ozlem Ozcan Simsek et al.

Summary: Cancer is a polygenic disease, and utilizing genomic data combined with gene expression data can help accurately differentiate different cancer types. A novel gene selection method was developed using existing mutation information, and experimental results showed its comparable or improved performance compared to traditional methods.

BIOLOGY DIRECT (2021)

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Article Computer Science, Artificial Intelligence

A two-stage hybrid ant colony optimization for high-dimensional feature selection

Wenping Ma et al.

Summary: The paper introduces a two-stage hybrid ACO algorithm for high-dimensional feature selection, which is capable of handling large-scale datasets efficiently with shorter running time.

PATTERN RECOGNITION (2021)

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Review Automation & Control Systems

Review of swarm intelligence-based feature selection methods

Mehrdad Rostami et al.

Summary: Research has shown that feature selection methods can improve the accuracy of data mining tasks and reduce computational complexity by reducing irrelevant, redundant, or noisy data to lower data dimensionality. The aim of feature selection is to select a subset of features with the lowest inner similarity and highest relevancy to the target class.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2021)

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Proceedings Paper Automation & Control Systems