Call for papers for JDSSV Special Issue on Explainable Machine and Statistical Learning

The development of data analysis for large scale data and statistical learning methods for data science is gaining importance for researchers interested in extracting insight from data. To advance data science methods, collaboration between different scientific disciplines, such as, statistics, computer science, computational mathematics, physics, social sciences, economics, amongst others is needed to develop methodologies and approaches.

For this special issue on Explainable Machine and Statistical Learning with guest editors Tomaso Aste (University College London), Paola Cerchiello (University of Pavia), Nicola Torelli (University of Trieste) and Rosanna Verde (University of Campania “Luigi Vanvitelli” ), we call for papers treating themes related to the modeling and analysis of complex data (structured, non-structured, mixed), using data analytics, statistical learning, and machine learning methods. Submissions are encouraged that propose novel approaches and visualization tools to provide the explainability of such models, particularly in real applications. Finally, papers emphasizing multidisciplinary topics are especially welcome. 

Submissions to this special issue should be done at jdssv.org following the standard requirements of the journal and by selecting “Special Issue on Explainable Machine and Statistical Learning.” Submissions will follow the standard JDSSV peer review process. Submission is open until July 31, 2023. We offer dual submissions, that is, papers that do not fit into the Special Issue will automatically be transferred to the regular submission channel unless the author specifies to submit only to the Special Issue. Papers will enter the review process immediately upon receipt (i.e., guest editors will not wait until the end of the submission window to start the review process). We intend to publish all accepted papers as a single special issue in 2024 after finalization of the review process.