🔒SCI: Call for Papers-Advanced Algorithms for Feature Selection in Machine Learning
Journal: CMC-Computers, Materials & Continua (SCI IF=2.0)
🔗SI URL: https://www.techscience.com/cmc/special_detail/feature_selection
📅 Submission Deadline: 30 July 2025
🌟 Guest Editors:
Dr. Muhammad Adnan Khan, Gachon University, 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.
📚Key Words:
Feature Selection, Machine Learning, Dimensionality Reduction, Hybrid Methods, Sparse Learning, Optimization Algorithms, High-Dimensional Data
One of the main challenges in feature selection for machine learning is determining the optimal combination of feature selection techniques and models. This is particularly problematic as there is no guarantee of the accuracy or effectiveness of a given combination. Different feature selection methods, such as filter, wrapper, and embedded techniques, may perform variably across different models and datasets. This unpredictability necessitates extensive experimentation and validation to identify the best combinations, highlighting the need for more research into adaptive and context-aware feature selection algorithms that can dynamically adjust to different modeling scenarios and data characteristics.
Determining the optimal combination of feature selection techniques and models in machine learning is challenging due to the variability in performance across different datasets. Extensive experimentation is required, emphasizing the need for adaptive feature selection algorithms.
- Badges
- Science topic
- Similar topics
- Engineering
- Modeling