Symmetry/Asymmetry in Data Mining and Machine Learning
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 31 January 2027 | Viewed by 827
Special Issue Editor
Interests: machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The Special Issue “Symmetry/Asymmetry in Data Mining and Machine Learning” explores the pivotal role of symmetry and asymmetry in research. While a symmetrical structure enables generalization, asymmetry captures the complexity and nuances inherent in real-world data. We invite original studies and reviews investigating how symmetry and asymmetry influence algorithm design, feature selection, model training, or data mining and machine learning.
Dr. Nagender Aneja
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- model generalization
- symmetry in machine learning
- asymmetry in machine learning
- real-world data complexity
- data representation
- pattern recognition
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