Guest Editors
Dr. Yu Zhou, Shenzhen University, China
Dr. Jaesung Lee, Chung-Ang University, South Korea
Dr. Xiao Zhang, South-Central Minzu University, China
Dr. Eneko Osaba, TECNALIA Research & Innovation, Spain
Summary
Machine learning (ML) or deep learning (DL) methods have demonstrated their great success in the past ten years for various applications, such as computer vision, bioinformatics, healthcare and transportation. For real-wolrd applications, current ML or DL models still suffer from high-dimensionlity issue, data uncertainty and lack of global convergence and interpretability. Recent development of soft computing approaches, such as evolutionary computation, swarm intelligence and fuzzy systems have shown good potential in modeling and optimizing the above issues and it is very interesting to investigate the following aspects:
1) Soft computing methods to conduct data processing
2) Soft computing methods to improve the generalization of the learning model
3) Soft computing methods to assist the decision making.
The topics include those related to the combination of soft computing and ML/DL, but not limited to, the following:
Dimension reduction or feature selection by soft computing methods
Soft computing for supervised, semi-supervised and unsupervised learning
Data processing, mining and analysis through soft computing methods
Neural achitechture search via soft computing
Soft computing with ensemble learning
Deep learning model interpretability
Soft computing with reinforcement learning
Fuzzy classification and clustering
Evolutionary machine learning and decision making
Applications in healthcare, transportation, sports, smart city and etc.
Keywords
Soft computing, computational intelligence, machine learning
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