The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases will be held in Grenoble, France, from September 19th to September 23rd, 2022.

This is Europe's top machine learning and data mining conference, with over 19 years of successful events and conferences around Europe.

The major tracks will be Research and Applied Data Science, as in prior years.

In research track, we welcome research articles in all fields of machine learning, knowledge discovery, and data mining. Following in the footsteps of ECML PKDD, we are looking for high-quality papers in terms of novelty, technical excellence, potential impact, and presentation clarity. Papers should demonstrate that they make a substantial contribution to the field (e.g, improve the state-of-the-art or provide a new theoretical insight).

In Applied Data Science track, We seek articles that highlight unique applications of machine learning, data mining, and knowledge discovery to real-world problems, bridging the gap between practice and current theory. Papers should explicitly describe the real-world difficulty being addressed (including any idiosyncrasies of the data, such as data set size, noise levels, sample rates, and so on), the methodology employed, and the conclusions derived for the use case.

Authors are encouraged to follow Reproducible Research (RR) best practices by making data and software tools available for reproducing the results presented in their articles. We require the use of standard repository hosting services for data sets, such as dataverse (https://dataverse.org/), mldata (https://www.mldata.io/datasets/), openml (https://www.openml.org/), and mloss (http://mloss.org/software/), bitbucket (https://bitbucket.org/), and github (https://github.com/) for source code.


* Abstract Submission Deadline: 30 March 2022
* Paper Submission Deadline: 06 April 2022
* Author Notification: 14 June 2022
* Camera Ready Submission: 1 July 2022
The deadline on each of these dates is 23:59 (AoE).

Three reviewers will assess each submission for novelty, technical excellence, potential impact, and clarity.
At least one reviewer from industry will be assigned to the applied data science track. ECML-PKDD has a long history of being a really varied conference that covers a wide range of Machine Learning and Data Mining subjects. To preserve this, the selection procedure takes into account the range of themes.

Submissions will only be evaluated in the track in which they were submitted, and they will not be moved across tracks.

The review procedure is double-blind (reviewers and area chairmen do not know who the authors are; reviewers do see each other’s names). Papers must not contain any author identifying information (names, affiliations, etc. ), self-references, or links (e.g., github, Youtube) that reveal the authors’ identities (e.g., references to own work should be given neutrally like other references, without mentioning ‘our previous work’ or similar).
However, we realize that there are limitations to what can be done in terms of anonymization; for instance, if you utilize data from your own company and it is important to the article, you may mention the company.

Only papers that were presented at the conference will be included in the proceedings.