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

Dear Colleagues,

Machine learning (ML) has been widely applied for big data processing and analytics, where various optimization problems (regarding 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 vector pruning and ensemble learning). It is of great interest to investigate the combination of EC and ML in solving large-scale big data analytical problems.

The interdisciplinary research of this topic focuses on the progress of machine learning and 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 aim to disseminate knowledge among researchers, designers, and users in this exciting field.

This Special Issue is the second edition of the Special Issue “Symmetric Machine Learning Method Enhanced by Evolutionary Computation and Its Applications in Big Data Analytics” (https://www.mdpi.com/journal/symmetry/special_issues/Symmetric_Machine_Learning)

Prof. Dr. Shangce Gao
Prof. Dr. Lianbo Ma
Guest Editors

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