Recent Advances in Machine Learning Methods for Imperfect Large-Scale Data

Dear Colleagues,

In recent years, large-scale datasets have become a crucial foundation for machine learning-based research and applications. However, the data available in real applications are often imperfect, containing noise, missing values, and imbalanced classes, to name just a few. Data continuously accumulate over time, leading to new challenges, such as expanding label spaces, shifting statistical properties, and increasing model training costs. Extracting valuable information from such imperfect large-scale data while optimizing model efficiency has become a research hotspot in the field of machine learning.

This Special Issue aims to collect and showcase the latest research achievements in handling and analyzing imperfect large-scale data, with a focus on innovative methods for improving model efficiency. Topics of interest include, but are not limited to, the following:

1. Machine learning algorithms with noisy data;
2. Machine learning algorithms with missing data;
3. Machine learning algorithms with imbalanced data;
4. Machine learning algorithms with incomplete data;
5. Training methods for solving catastrophic forgetting;
6. Machine learning algorithms for solving concept drift;
7. Efficient training methods for large-scale data;
8. Privacy preservation and security.

We look forward to receiving your contributions.

Dr. Ximing Li
Dr. Bo Fu
Dr. Changchun Li
Guest Editors

Manuscript Submission Information

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• data cleaning
• missing data
• imbalanced data
• weakly supervised learning
• incremental learning
• concept drift
• lightweight model
• privacy preservation