Call for Papers: Special Issue on Discovery Science of the Machine Learning Journal

Scope and Background:

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The Machine Learning journal invites submissions on Discovery Science - a research discipline concerned with the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, and intelligent data analysis, as well as their applications in various scientific domains. Submissions focusing on the analysis of different types of complex data, such as structured, spatio-temporal, network and social-network data are welcome. Submissions addressing applications in scientific domains, such as environmental and life sciences are also welcome. Finally, submissions from the areas of computational scientific discovery, mining scientific data, computational creativity and discovery informatics are encouraged.

Topics of interest include:

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•       computational scientific discovery

•       mining scientific data

•       machine learning and statistical methods

•       data mining and knowledge discovery

•       data and knowledge visualization

•       knowledge discovery from scientific literature

•       mining text, unstructured and multimedia data

•       mining graphs, networks, structured and relational data

•       mining temporal and spatial data

•       mining data streams

•       network analysis

•       discovery informatics

•       discovery and experimental workflows

•       knowledge capture and scientific ontologies

•       planning to Learn

•       data streams, evolving data and models

•       active knowledge discovery

•       human-machine interaction for knowledge discovery and management

•       evaluation of models and predictions in discovery setting

•       causal inference in machine learning

•       interpretability and explainability of machine learning and deep learning models

•       data and knowledge integration

•       logic and philosophy of scientific discovery

•       computational creativity

•       applications of the above methods in various scientific domains (e.g., bioinformatics, system biology, and environmental informatics)

Papers which, at the time of submission, have appeared in the proceedings of Discovery Science 2019 (or other relevant conferences) will be considered provided that the submission contains 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. Authors of such submissions are required to enclose an accompanying letter discussing in detail the differences between the conference paper and their MLJ submission. The guest editors will make the decision on whether the difference is significant enough to warrant publication. Once accepted, the journal version should include a short paragraph explaining how it extends the previously published conference paper.

Schedule:

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Paper submission: 14th February 2020

First notifications: 28th April 2020

Deadline for revised submissions: 13th June 2020

Final notifications: 28th July 2020

Expected publication: September 2020 (online)


Submission instructions:

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Resources for journal authors, including templates and style files, as well as frequently asked questions can be found at:  Journal Author Resources <https://www.springer.com/gp/authors-editors/journal-author/frequently-asked-questions/3832>.

Springer does not require authors to submit their papers in a prescribed template.  If the paper is accepted for publication the source files will be converted by the typesetter and prepared in Springer's format for our online platform, SpringerLink.

Submissions should be made via the Machine Learning journal website (http://www.editorialmanager.com/mach/default.aspx). An article is submitted to the Discovery Science 2019 special issue by choosing “S.I.: Discovery Science 2019” as the article type: This choice will be available at the submission website from 14th January 2019.

Guest editors:

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Petra Kralj Novak, Jožef Stefan Institute, Ljubljana, Slovenia

Tomislav Šmuc, Rudjer Bošković Institute, Zagreb, Croatia