Applications of Machine Learning with White-Boxing
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 30 June 2025 | Viewed by 1408
Special Issue Editor
Special Issue Information
Dear Colleagues,
AI is used to build devices and machines that can learn and mimic cognitive the functions of humans. ML is the discipline that develops and investigates methods for predicting a response variable Y based on data collected on Y and potential predictors or features X1, X2, ..., Xp. If the response Y is binary or categorical with only a few labels, the ML method is called a classifier. If data have no Y or labels, the ML method is referred to as an unsupervised learning method. If the response Y is continuous, the ML methods behave like linear or non-linear regression methods. ML provides the predictions that are needed by AI.
ML prediction methods are called Black-Box routines since they only provide a predicted value, with no explanation or interpretation of results. White-Boxing, another ML method, provides some explanation of how the outputs are related to the features.
This Special Issue aims to explore the cutting-edge applications of White-Box machine learning models in AI systems. It provides a platform for researchers, practitioners, and experts to share their innovative research, methodologies, and insights on the application and implications of White-Box approaches in machine learning.
Dr. Ashok K. Singh
Guest Editor
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Keywords
- machine learning
- artificial intelligence
- white-box models
- interpretability
- prediction
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