Call for Papers: Special Issues of Machine Learning and Data Mining and Knowledge Discovery (Springer Journals)
We invite submissions for the journal track of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2023. The journal track of the conference is implemented in partnership with the Machine Learning Journal and the Data Mining and Knowledge Discovery Journal.
Subject Coverage
We invite the submission of high-quality manuscripts reporting relevant research studies on all topics related to machine learning, knowledge discovery, and data mining.
Eligibility Criteria
Given the special nature of the journal track submitted papers have to adhere to the following eligibility criteria:
- Papers have to satisfy the high-quality criteria of journal papers and at the same time lend themselves to conference talks.
- Journal versions of previously published conference papers and survey papers will not be considered for the special issue.
- 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 to the Springer journals.
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.
Time scale
The journal track allows continuous submissions from November 2022 to February 2023. Papers will be processed and sent out for review after each of the following two cutoff dates:
- December 7, 2022
- February 10, 2023
Submission Procedure
We require authors to submit their paper to the Springer Editorial Manager. Specifically, authors make a journal submission to either the Springer Data Mining and Knowledge Discovery journal (https://www.editorialmanager.com/dami/default.aspx) or the Springer Machine Learning journal (https://www.editorialmanager.com/mach/default.aspx), and select the type of submission to be for the ECML PKDD 2023 special issue.
It is recommended that submitted papers do not exceed 25 pages, including references. Every paper may be accompanied by unlimited appendices (a part of the same pdf file). The papers should follow the submission guidelines on the respective journals’ home pages. Templates can be found at https://www.springernature.com/gp/authors/campaigns/latex-author-support.
Both journals require authors to include an information sheet (for Machine learning submissions) or a cover letter (up to 2 pages) as 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 research area?
- 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.
- Specify 5 general keywords and five specific keywords describing the main research activity presented in the manuscript.
- Declare any conflict of interest by reporting the email domains of all institutions with which the authors have an institutional conflict of interest. Authors have an institutional conflict of interest if they are currently employed or have been employed at this institution in the past three years, or if the authors have extensively collaborated with this institution within the past three years. Authors are also required to identify all Guest editorial Board Members with whom the authors have a conflict of interest. Examples of conflicts of interest include co-authorship in the last five years, a colleague in the same institution within the last three years, and advisor/student relationships.
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.
Submission site and paper format
Papers should be prepared in English and structured in accordance with the Springer journal requirements. Author instructions and style files are available at (submission guidelines for Machine Learning https://www.springer.com/journal/10994/submission-guidelines?IFA, Submission guidelines for Data Mining and Knowledge Discovery https://www.springer.com/journal/10618/submission-guidelines ).
Manuscripts have to be submitted through the Editorial Manager provided by Springer (Submit to Machine Learning , submit to Data Mining and Knowledge Discovery.
Authorship
The author list as submitted with the paper is considered final. No changes to this list may be made after paper submission, either during the review period or in case of acceptance, at the final publication stage.
Paper presentation at ECML-PKDD 2023
Authors submitting their work to the Journal Track @ ECML PKDD commit themselves to present their paper at the ECML PKDD 2023 conference if accepted.
Manuscripts submitted to the journal Track that will receive the final acceptance decision by July 15, 2023, will be presented at ECML-PKDD 2023 in Turin. Papers receiving the acceptance decision after the middle of July will be presented at ECML-PKDD 2024.
Contact
For further information, please contact Mail: ecml-pkdd-2023-journal-track-chairs@googlegroups.com