Final deadline for Journal Track submissions 28th January. Please note submissions made after Sunday 28th January will not be considered for review for the 2018 Journal Track.


We invite submissions for the journal track of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) 2018. The journal track of the conference is implemented in partnership with the Machine Learning Journal and the Data Mining and Knowledge Discovery Journal. The conference provides an international forum for the discussion of the latest high-quality research results in all areas related to machine learning, data mining, and knowledge discovery.

Papers on all topics related to machine learning, knowledge discovery, and data mining are invited. However, given the special nature of the journal track, only papers that satisfy the quality criteria of journal papers and at the same time lend themselves to conference talks will be considered. Consequently, journal versions of previously published conference papers, or survey papers will not be considered for the special issue. Papers that do not fall into the eligible category may be rejected without formal reviews but can of course be resubmitted as regular papers.

Authors are encouraged to adhere to the best practices of Reproducible Research (RR), by making available data and software tools for reproducing the results reported in their papers. For the sake of persistence and proper authorship attribution, we require the use of standard repository hosting services such as dataverse, mldata, openml, etc. for data sets, and mloss, bitbucket, github, etc. for source code.

Authors who submit their work to the ECMLPKDD special issues of these journals commit themselves to present their paper at the ECMLPKDD 2018 conference if it is accepted.

The journal track allows continuous submissions from the end of August 2017 to the end of January 2018. Papers will be processed and sent out for review after each of the following five cutoff dates:

  • August 27, 2017
  • October 1, 2017
  • November 5, 2017
  • December 10, 2017
  • January 28th, 2018

The deadline on each of these dates is midnight, Central European Time.

We strive for a high quality and efficient review process. Each submission will be evaluated by three experienced reviewers including members of the Guest Editorial Board. Our goal is to arrive an initial decision about 8 weeks after each cutoff date, though meeting this target may not always be possible. After the initial review phase, many papers will require substantial revisions, and the revised paper will be re-reviewed, which extends the review process. Consequently, a paper’s chance of finishing the review cycle and being included in the ECMLPKDD 2018 special issue decreases with each subsequent cutoff date. Inclusion of the delayed papers in forthcoming special issues and conference editions is subject to approval of the respective Program and Journal track chairs.

To submit to this track, authors have to make a journal submission to either the Springer Data Mining and Knowledge Discovery journal or the Springer Machine Learning journal, and select the type of submission to be for the ECMLPKDD 2018 special issue. It is recommended that submitted papers do not exceed 20 pages including references and appendices, formatted in the Springer journal style (svjour3, smallcondensed). This is a soft limit, but if a submission exceeds the limit, please provide a brief justification regarding the length in the cover letter. Arriving at an initial decisions for papers over 20 pages may take longer.

Both journals require authors to include an information sheet (for Machine learning submissions) or a cover letter (up to 2 pages) as a supplementary material (for Data Mining and Knowledge Discovery submissions) that contains a short summary of their contribution and specifically address the following questions:

  • What is the main claim of the paper? Why is this an important contribution to the machine learning/data mining literature?
  • What is the evidence provided to support claims? Be precise.
  • Report 3-5 most closely related contributions in the past 7 years (authored by researchers outside the authors’ research group) and briefly state the relation of the submission to them.
  • Who are the most appropriate reviewers for the paper? Authors are required to suggest up to four candidate reviewers (especially if external to the Guest Editorial Board), including a brief motivation for each suggestion.
  • Optionally, list up to four researchers/potential reviewers with competing interests that should not be considered for reviewers.


Submit to Machine Learning  or Submit to Data Mining and Knowledge Discovery

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