Machine Learning with Applications: Dealing with Interpretability and Imbalanced Datasets

Dear Colleagues,

A major disadvantage of using machine learning is that insights about the data are hidden in increasingly complex models. Moreover, the best performing models are often ensembles which cannot be interpreted, even if each single model could be interpreted. Explainable Machine Learning (Explanatory Artificial Intelligence, XAI) summarizes the reasons for black-box behaviour with the aim to gain the trust of users.

This Special Issue of Electronics will provide a forum for discussing exciting research on applying Interpretable Machine Learning (IML) methods on data captured by sensors or generated by interaction of users with systems in a variety of domains. Both original research articles and comprehensive review papers are welcome. We invite also submissions dealing with imbalanced classification problem in which the distribution of examples across the known classes is biased or skewed.

Topics of Interest of this Special Issue include, but are not limited to

• Interpretability (intrinsic or post hoc)
  • Global model interpretability
  • Local model interpretability
• Feature selection techniques
• Imbalanced classification
• Explainable AI decision support systems
• Real-world applications of Interpretable Machine Learning in areas such as:
  • Intelligent transportation systems
  • Food safety
  • Agriculture
  • Natural Language Processing
  • Education
  • Healthcare
  • Finance
  • Smart cities

Prof. Dr. Maja Matetic
Prof. Dr. Xiaoshuan Zhang
Dr. Marija Brkić Bakarić
Guest Editors

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