Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.2
Journals
Symmetry
Special Issues
Symmetry/Asymmetry in Data Mining & Machine Learning
share announcement

Symmetry/Asymmetry in Data Mining & Machine Learning

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 643

Special Issue Editors

Dr. Yunchao Song

E-Mail Website
Guest Editor
College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommuncations, Nanjing 210023, China
Interests: 6G wireless communication; MIMO communication; signal procesing
Dr. Zhisheng Yin

E-Mail Website
Guest Editor
The State Key Laboratory of ISN, The School of Telecommunications Engineering, Xidian University, Xi’an 710071, China
Interests: space–air–ground integrated networks; wireless communications; AI; physical-layer security
Special Issues, Collections and Topics in MDPI journals
Dr. Kuiyu Chen

E-Mail
Guest Editor
College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Interests: automatic modulation recognition; parameter estimation; anti-jamming technology

Special Issue Information

Dear Colleagues,

Symmetry and asymmetry, as central concepts in understanding the laws of nature, play an increasingly significant role in data mining and machine learning. To better advance research in this area, it is crucial to uncover the unknown symmetry and asymmetry properties inherent in data, models, and other components. Equally important is the development and analysis of techniques that incorporate symmetry and asymmetry principles in data processing and model design. We invite contributions addressing three key aspects: (1) discovery of symmetry/asymmetry patterns, (2) development of symmetry/asymmetry-based methods and models, and (3) comprehensive analysis of symmetry/asymmetry effects in data mining and machine learning systems.

Dr. Yunchao Song
Dr. Zhisheng Yin
Dr. Kuiyu Chen
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • data mining
  • machine learning
  • symmetry/asymmetry theory and analysis
  • symmetry/asymmetry property discovery
  • symmetry/asymmetry-inspired training
  • symmetry/asymmetry-inspired model
  • dynamic symmetry-breaking data analysis
  • multimodal data/signal fusion
  • data/signal classification and recognition

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 1593 KB  
Article
Can Tree-Based Models Improve GAPC Mortality Models’ Forecasting Accuracy?
by Özer Bakar and Murat Büyükyazıcı
Symmetry 2025, 17(9), 1540; https://doi.org/10.3390/sym17091540 - 15 Sep 2025
Viewed by 285
Abstract
Generalized age–period–cohort (GAPC) models are mortality models that incorporate stochasticity, which can be represented in a generalized linear or non-linear context. By fitting the data to either mortality model, one can make forecasts for the future under the extrapolation framework. Previous research indicates [...] Read more.
Generalized age–period–cohort (GAPC) models are mortality models that incorporate stochasticity, which can be represented in a generalized linear or non-linear context. By fitting the data to either mortality model, one can make forecasts for the future under the extrapolation framework. Previous research indicates that tree-based machine learning (ML) methods are suitable for improving the forecasting ability of such mortality models using different training/testing time periods. However, there is no consensus about generalizing this phenomenon to the improvement of fitted/forecasted mortality rates without depending on a particular mortality model or the model’s training/testing period. Furthermore, GAPC models assume symmetry of the interaction between the features and the mortality rates. Tree-based ML methods can capture asymmetric relationships within demographic data and complement the rigid assumption of symmetry of stochastic mortality models. The objective in our study is to re-estimate the mortality rates obtained from each mortality model by applying tree-based machine learning (ML) methods within a procedure that creates a suitable environment to improve the forecasting accuracy of each GAPC model. By combining mortality models with tree-based methods, both the interpretability of the parameters of mortality models and the features used within machine learning methods can be ensured. In the application carried out in this study for Denmark and Sweden, the results show that all tree-based ML-integrated models reduced the error (root mean squared error) compared to each pure mortality model. This study shows that if the proper procedure is applied, the forecasting ability of each mortality model can be improved. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Data Mining & Machine Learning)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop