Guest Editors
Dr. Man-Fai Leung
Email: man-fai.leung@aru.ac.uk
Affiliation: Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, CB1 1PT, United Kingdom
Homepage:
Research Interests: Information Fusion, Knowledge Discovery, Neural Networks
Prof. Athanasios V. Vasilakos
Email: thanos.vasilakos@uia.no
Affiliation: Center for AI Research (CAIR), University of Agder (UiA), 4879 Grimstad, Norway
Homepage:
Research Interests: Feature fusion, Data Science, Machine Learning
Summary
Feature engineering plays a crucial role in enhancing knowledge discovery within Computational Intelligence (CI). It significantly increases the efficiency and accuracy of data analysis. This process is fundamental across various applications, as it identifies the most valuable data attributes from vast datasets, thereby facilitating improved decision-making. The integration of CI techniques such as fuzzy logic, genetic algorithms, and neural networks significantly enhances the effectiveness of feature engineering. This refinement improves predictive performance and enables systems to handle evolving data dynamics effectively, leading to more insightful and actionable outcomes.
A key challenge in this field is the development of feature engineering algorithms, particularly those for feature selection, that can process extensive data without losing speed or accuracy. This special issue calls for research that develops and evaluates innovative feature selection methods tailored to CI applications. We seek contributions that demonstrate these methods in practical settings, showing how they can lead to more precise knowledge discovery.
This special issue invites original research papers, reviews, and case studies that delve into, but are not limited to, the following topics:
- Advanced algorithms for feature selection in computational intelligence
- Optimization techniques for neural networks to enhance feature extraction
- Application of evolutionary algorithms in feature selection
- Techniques to scale feature selection methods for large datasets
- Enhancing the interpretability and transparency of feature selection processes
- Collaborative approaches in AI to refine feature selection practices
- Analyzing behavioral models and the impact of feature importance
- Comprehensive benchmarking and comparisons of feature selection algorithms
- Interdisciplinary applications of feature engineering techniques in real-world scenarios
Login
Register
Submit a Paper
Propose a Special lssue