For this special issue, 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. Submission is open until July 31, 2023.Read more about Call for papers for JDSSV Special Issue on Explainable Machine and Statistical Learning
About the Journal
The journal welcomes contributions to practical aspects of data science, statistics and visualisation, and in particular those which are linking and integrating these subject areas. Papers should thus be oriented towards a very wide scientific audience, and can cover topics such as machine learning and statistical learning, the visualisation and verbalization of data, big data infrastructures and analytics, interactive learning, advanced computing, and other important themes. JDSSV is an open access journal that charges no author fees.
This international refereed journal creates a forum to present recent progress and ideas in the different disciplines of data science, statistics, and visualisation. It welcomes contributions to data science, statistics, and visualisation, in particular, those aspects which link and integrate these subject areas. Articles should be oriented towards a wide scientific audience, and can cover topics such as machine learning and statistical learning, the visualisation and verbalisation of data, visual analytics, big data infrastructures and analytics, interactive learning, and advanced computing. Papers that discuss two or more research areas of the journal are favoured. Scientific contributions should be of a high standard. The journal explicitly welcomes contributions that include software with the aim of reproducibility of the results and application of the proposed methodology to other data by the reader. It is expected that data used in a paper are provided. JDSSV is an open access journal that charges no author fees.