Machine Learning Approaches for Prediction and Decision Making
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 28
Special Issue Editors
Interests: investment decision support; state modelling; machine learning; algorithmic trading
Interests: statistical data modelling; big data analytics; Bayesian inference; Markov Chain Monte Carlo (MCMC) simulation; machine learning; decision support systems; system reliability modelling; degradation modelling and condition prediction; optimization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The MDPI Information journal invites submissions to a Special Issue titled “Machine Learning Approaches for Prediction and Decision Making”.
Machine learning (ML) techniques are rapidly advancing in both development and application. With the increasing need to analyze large datasets, ML-based techniques enable the discovery of complex patterns and support both automated and human-assisted decision-making processes with growing accuracy and reliability. These methods find applications in computer science, medicine, economics, industry, and many other fields.
However, both the theoretical and applied aspects of machine learning require continuous development to meet new challenges and rising expectations. Therefore, it is essential to develop new methodologies, modify existing algorithms, and advance optimization techniques that improve the effectiveness and efficiency of ML models.
The aim of this Special Issue is to present the latest research on the use of machine learning in predictive and decision-making tasks. We welcome submissions of both theoretical articles covering, among other topics, the development of methods and the design and optimization of ML algorithms, as well as applied studies that present innovative solutions to current problems in technology, the economy, and other areas using machine learning methods.
Topics of Interest
- Predictive Modeling for Complex Decision-Making Environments;
- Integration of Machine Learning and Optimization Techniques;
- Explainable and Interpretable Machine Learning for Decision Support Systems;
- Time Series Forecasting and Decision Making;
- Comparative Evaluation of Predictive Algorithms in Decision Support Systems;
- Predictive Modeling and Pattern Recognition;
- Optimization Techniques for Prediction Accuracy.
Papers should be formatted according to the MDPI Information journal template. Complete instructions for authors can be found at https://www.mdpi.com/journal/information/instructions.
Dr. Michał Stasiak
Dr. Tieling Zhang
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. Information 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 1800 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
- machine learning
- decision support
- pattern recognition
- predictive modeling
- decision support systems
- supervised learning
- unsupervised learning
- reinforcement learning
- explainable AI
- time series forecasting
- deep learning
- model interpretability
- predictive optimization
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.
