Guest Editors
Dr. Muhammad Adnan Khan, Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam, 13120, Republic of Korea
Summary
This special issue examines the critical role of advanced feature selection algorithms in enhancing the efficacy of machine learning models. Feature selection is pivotal in the machine learning pipeline, significantly influencing model performance by improving accuracy, reducing computational complexity, enhancing interpretability, and mitigating overfitting. However, the growing complexity and volume of data necessitate innovative solutions to efficiently and effectively select relevant features.
The research in feature selection faces several significant challenges. Traditional methods may struggle with high-dimensional data, and the emergence of new data types and structures demands more sophisticated techniques. Moreover, the need for interpretable and explainable models in various domains further underscores the importance of robust feature selection methods. This special issue aims to:
· Explore innovative algorithms and techniques for feature selection.
· Assess the effectiveness of these techniques in diverse applications.
· Foster interdisciplinary collaboration among researchers, practitioners, and policymakers.
We especially encourage submissions that present novel methodologies, evaluate their performance in real-world scenarios, and propose practical frameworks for enhancing machine learning models through effective feature selection. We welcome original research articles, review papers, and case studies. The special issue solicits original research articles, reviews, and case studies that cover, but are not limited to, the following topics:
· Hybrid and ensemble feature selection techniques
· Scalable algorithms for high-dimensional and big data feature selection
· Domain-specific feature selection methods (e.g., text, bioinformatics, computer vision)
· Interpretable and explainable feature selection approaches
· Automated and meta-learning approaches to feature selection
· Quantum computing and other emerging paradigms for feature selection
· Federated and privacy-preserving feature selection techniques
· Benchmarking and comparative studies of feature selection algorithms
· Real-world applications and case studies of advanced feature selection methods
Keywords
Feature Selection, Machine Learning, Dimensionality Reduction, Hybrid Methods, Sparse Learning, Optimization Algorithms, High-Dimensional Data
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