Evolutionary Computation for Feature Selection and Dimensionality Reduction

Dear Colleagues,

This Special Issue "Evolutionary Computation for Feature Selection and Dimensionality Reduction” delves into the intricate fusion of evolutionary computation in the realms of feature selection in machine learning and feature map selection in deep learning. Feature selection and feature map selection are important data processing techniques to shallow learning and deep learning methods. They can significantly improve the performance of learning algorithms in terms of the accuracy and learning speed while also reducing their size. However, they are challenging tasks due to the large search space.

This Special Issue aims to investigate both the new theories and methods in different evolutionary computation/machine learning paradigms, focusing on feature selection in shallow learning and feature map selection in deep learning. Evolutionary computation paradigms include, but are not limited to, particle swarm optimization, artificial bee colony optimization, genetic algorithm, and differential evolution, while those for machine learning include, but are not limited to, MLP, CNN, and genetic programming. This Special Issue also welcomes novel applications of EC-based/learning-based feature selection methods in related fields. For all the aforementioned issues, we kindly invite the scientific community to contribute to this Special Issue by submitting novel and original research related, but not limited, to the following topics:

• Feature selection;
• Feature map selection;
• Learning-based optimization;
• Dimensionality reduction;
• Swarm intelligence optimization;
• Evolutionary computation;
• Learning-based feature selection;
• Evolutionary feature selection;
• Feature extraction;
• Feature dimensionality reduction on high-dimensional and large-scale data;
• Evolutionary feature selection and construction;
• Multi-objective feature selection;
• Feature selection for clustering;
• Feature selection for multi-task optimization and multi-task learning;
• Hybridization of feature selection and cost-sensitive classification/clustering;
• Hybridization of feature selection and class-imbalance classification/clustering;
• Applications of feature selection;
• Genetic algorithm/genetic programming/particle swarm optimization/ant colony optimization/artificial bee colony/differential evolution/fireworks algorithm/brain storm optimization for feature selection;
• Machine learning/data mining/neural network/deep learning/decision tree/deep neural network/convolutional neural network/reinforcement learning/ensemble learning/K-means for feature selection/ feature map selection;
• Real-world applications of feature selection, e.g., images and video sequences/analysis, face recognition, gene analysis, biomarker detection, medical data analysis, text mining, intrusion detection systems, vehicle routing, computer vision, natural language processing, speech recognition, etc.

Prof. Dr. Yu Xue
Prof. Dr. Yong Zhang
Prof. Dr. Bing Xue
Prof. Dr. Ferrante Neri
Guest Editors

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• feature selection
• feature map selection
• learning-based optimization
• dimensionality reduction
• swarm intelligence optimization
• evolutionary computation
• learning-based feature selection