The Machine Learning Journal is currently accepting submissions for its Special Issue on Discovery Science 2023. This special issue calls for research papers that address all aspects of discovery science, a research field that focuses on developing and analysing methods for discovering scientific knowledge using machine learning, data mining, intelligent data analysis, and big data analytics. The journal also welcomes papers that apply these methods in various domains.

Submissions addressing all aspects of discovery science are welcome. Research papers that focus on the analysis of different types of massive and complex data, including structured, spatio-temporal and network data, as well as heterogeneous, continuous or imprecise data are encouraged. Research papers in the fields of computational scientific discovery, mining scientific data, computational creativity and discovery informatics are also welcome. Finally, submissions addressing applications of artificial intelligence in different domains of science, including biomedicine and life sciences, materials science, astronomy, physics, chemistry, as well as social sciences are encouraged.

Possible topics include, but are not limited to:

• Artificial intelligence (machine learning, knowledge representation and reasoning, natural language processing, statistical methods, etc.) applied to science
• Knowledge discovery and data mining
• Causal modelling
• AutoML, meta-learning, planning to learn
• Machine learning and high-performance computing, grid and cloud computing
• Literature-based discovery
• Ontologies for science, including the representation and annotation of datasets and domain knowledge
• Explainable AI, interpretability of machine learning and deep learning models
• Process discovery and analysis
• Computational creativity
• Anomaly detection and outlier detection
• Data streams, evolving data, change detection, concept drift, model maintenance
• Network analysis
• Time-series analysis
• Learning from complex data
• Graphs, networks, linked and relational data
• Spatial, temporal and spatiotemporal data
• Unstructured data, including textual and web data
• Multimedia data
• Data and knowledge visualization
• Human-machine interaction for knowledge discovery and management
• Evaluation of models and predictions in discovery setting
• Applications of the above techniques in scientific domains, such as Physical sciences (e.g., materials sciences, particle physics)Life sciences (e.g., systems biology/systems medicine)Environmental sciences
• Natural and social sciences

Papers which, at the time of submission, have appeared in the proceedings of Discovery Science 2023 or other relevant conferences will be considered provided that the submission constitutes a significant contribution beyond the conference paper containing at least 30% of new material (e.g., extensions of the method, additional technical results, etc.) as compared to the conference version of the paper. The guest editors (accounting for reviewers’ comments) will make the decision on whether the difference is significant enough to warrant publication. The journal version should include a short paragraph explaining how it extends the previously published conference paper.

Schedule

– Paper submission deadline: March 4, 2024
– First notification of acceptance: May 27, 2024
– Deadline for revised submissions: July 15, 2024
– Final notification of acceptance: August 30, 2024
– Expected publication date (online): October/November 2024

For further details see https://www.springer.com/journal/10994/updates/26362698

Guest editors

Rita P. Ribeiro, University of Porto & INESC TEC, Portugal
Albert Bifet, University of Waikato, New Zealand
Ana Carolina Lorena, Aeronautics Institute of Technology, Brazil