Conference Information
ECML-PKDD 2021: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
https://2021.ecmlpkdd.org
Submission Date:
2021-05-02
Notification Date:
Conference Date:
2021-09-13
Location:
Bilbao, Spain
Years:
25
CCF: b   CORE: a   QUALIS: a2   Viewed: 32725   Tracked: 207   Attend: 18

Conference Location
Call For Papers
We invite submissions for the journal track of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) 2021. 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.

Eligibiliy criteria:

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. Rejected papers from the journal track can be submitted to the conference.

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, e.g. dataverse (https://dataverse.org/), mldata (https://www.mldata.io/datasets/), openml (https://www.openml.org/ for data sets, and mloss (http://mloss.org/software/), bitbucket (https://bitbucket.org/), github (https://github.com/) 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 2021 but for the later deadlines this may be a later edition of the conference. 
Last updated by Dou Sun in 2020-09-03
Acceptance Ratio
YearSubmittedAcceptedAccepted(%)
201736410127.7%
201646012326.7%
201548312125.1%
201455011520.9%
201344711124.8%
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