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) 2020. 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. Note that a paper rejected by the Machine Learning Journal should not be submitted to the Data Mining and Knowledge Discovery Journal and vice versa. 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 presenting their paper at the ECMLPKDD conference if it is accepted. Note that for the earlier deadlines, this is likely to be ECMLPKDD 2020, but for the later deadlines this may be a later edition.


The journal track allows continuous submissions from September 2019 to mid May 2020. Papers will be processed and sent out for review after each of the following five cutoff dates:

  • September 6, 2019 September 13, 2019
  • November 8, 2019
  • January 10, 2020
  • May 15, 2020 (note that papers submitted by this last deadline cannot possibly be accepted in time for ECMLPKDD 2020)

The deadline on each of these dates is 23:59, Central European Time. We strive for a high quality and efficient review process. For each submission, we aim at obtaining three reviews from experienced reviewers, including members of the Guest Editorial Board. Our goal is to arrive at an initial decision about 10 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 2020 special issue decreases with each subsequent cutoff date, and for the May 15 deadline this is even impossible. This means that accepted papers, especially those that were submitted to the later deadlines, may be included in the ECMLPKDD 2021 (or even later) special issue (and subject to approval of the ECMLPKDD steering committee and future organizers). The reviewing process is single-blind.


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 (no paper can be submitted to both), and select the type of submission to be for the ECMLPKDD 2020 special issue. It is recommended that submitted papers do not exceed 20 pages including references. Every paper may be accompanied with unlimited appendices. The papers should be formatted in the Springer journal style (svjour3, smallcondensed). 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


For Queries relating to Journal Track Submissions email: jt_chairs[at]