Symmetric Machine Learning Method Enhanced by Evolutionary Computation and Its Applications in Big Data Analytics

Dear Colleagues,

Machine learning (ML) has been widely applied for big data processing and analytics, where various optimization problems (about model symmetry/asymmetry, model architecture and hyperparameters, data clustering, and data prediction) are frequently encountered. The automatic design of machine learning has become an increasingly popular research trend. Evolutionary computation (EC) is commonly used in these scenarios where classical numerical methods fail to find good enough solutions. Evolutionary approaches can be used in all the parts of ML: preprocessing (e.g., feature selection and resampling), learning (e.g., parameter setting and network topology), and postprocessing (e.g., decision tree/support vectors pruning and ensemble learning). It is of great interest to investigate the combination of EC and ML in solving large-scale big data analytic problems.

The interdisciplinary research of this topic focuses on the progress of machine learning, evolutionary algorithms and their applications for big data, as well as emerging intelligent applications and models in topics of interest, including, but not limited to, industrial control, job-shop scheduling, expert systems, pattern recognition, and computer vision.

This Special Issue aims to bring together both experts and newcomers from either academia or industry to discuss new and existing issues concerning evolutionary machine learning and big data, in particular, the integration between academic research and industry applications, and to stimulate further engagement with the user community. With this Special Issue, we want to disseminate knowledge among researchers, designers, and users in this exciting field.

Prof. Dr. Lianbo Ma
Prof. Dr. Shi Cheng
Prof. Dr. Shangce Gao
Dr. Yu Guo
Guest Editors

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