Data-Related Challenges in Machine Learning: Theory and Application
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 31 October 2025 | Viewed by 341
Special Issue Editors
Interests: application of deep learning in the fields of virtual reality (VR) and computer graphics
Interests: large language models; intrusion detection; wifi sensing; image processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning (ML) has transformed various industries, including healthcare, finance, cybersecurity, and autonomous systems. However, despite its success, ML still faces critical data-related challenges that impact its reliability, scalability, and fairness. This Special Issue, “Data-Related Challenges in Machine Learning: Theory and Application”, aims to explore fundamental issues, theoretical advancements, and innovative solutions addressing data quality, bias, privacy, efficiency, and interpretability in ML.
The special issue will focus on, but not be limited to, the following key challenges:
- Data Quality and Preprocessing: Handling noisy, missing, and inconsistent data remains a fundamental issue. Research on automated data cleaning, robust feature engineering, and data augmentation techniques is essential for improving ML model performance.
- Bias and Fairness in ML: Many ML systems inherit biases from their training data, leading to unfair outcomes. Ensuring fairness through bias detection, fairness-aware learning, and ethical AI development is crucial.
- Data Scarcity and Efficiency: In many real-world applications, collecting large, high-quality datasets is impractical. Methods such as few-shot learning, transfer learning, and self-supervised learning provide potential solutions.
- Privacy and Security in ML: As ML models handle sensitive information, privacy-preserving techniques like federated learning, differential privacy, and encrypted machine learning are gaining attention.
- Data Interpretability and Robustness: Understanding how ML models make decisions is essential for trust and adoption. Explainable AI (XAI) and adversarial robustness research contribute to making ML systems more transparent and reliable.
Prof. Dr. Kwang-Seong Shin
Dr. Sungkwan Youm
Prof. Dr. Seong-Yoon Shin
Guest Editors
Manuscript Submission Information
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Keywords
- data quality and preprocessing
- bias and fairness in machine learning
- few-shot learning and transfer learning
- privacy-preserving machine learning
- explainability and robustness in ML
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