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Information
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Machine Learning Approaches for Prediction and Decision Making
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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 1751

Special Issue Editors

Dr. Michał Stasiak

E-Mail Website
Guest Editor
Department of Investment and Real Estate, Poznan University of Economics and Business, 61-875 Poznan, Poland
Interests: investment decision support; state modelling; machine learning; algorithmic trading
Dr. Tieling Zhang

E-Mail Website
Guest Editor
Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW 2522, Australia
Interests: bockchain; big data analytics; machine learning; decision support systems; 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 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. 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

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  • 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 (2 papers)

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Research

17 pages, 1583 KB  
Article
Imputation of Multi-Dimensional High-Frequency Climate Data to Predict Air and Surface Temperatures in Kuwait
by Shehroz S. Khan and Rami Al-Hajj
Information 2026, 17(3), 221; https://doi.org/10.3390/info17030221 - 25 Feb 2026
Viewed by 350
Abstract
Missing values may arise in climate data collection due to sensor malfunction, transmission errors, device calibration and operational issues. This problem can be more catastrophic in the case of multi-dimensional and high-frequency climate data sets, where some or all climate readings could be [...] Read more.
Missing values may arise in climate data collection due to sensor malfunction, transmission errors, device calibration and operational issues. This problem can be more catastrophic in the case of multi-dimensional and high-frequency climate data sets, where some or all climate readings could be missing at multiple timestamps. These missing data in high-frequency climate modeling could lead to inaccurate prediction models, which in turn affect overall assessments, planning, and climate-related measures and policy. In this paper, we evaluate the performance of three imputation techniques based on the mean, k-nearest neighbor, time-based interpolation and a new temporal cross-year climate imputation approach using a random forest, long short-term memory (LSTM) model and contextual embedding-based Transformer regression methods. We discussed our findings on four years of multi-output, high-frequency and multi-dimensional climate data collected in Kuwait. Using a leave-one-year-out cross-validation approach, our results show that all imputation methods perform better than no imputation, with LSTM and time-based interpolation emerging as the best combination. Imputing climate data based on previous years’ timestamps did not yield good results, highlighting the variability of climate data across years. Full article
(This article belongs to the Special Issue Machine Learning Approaches for Prediction and Decision Making)
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18 pages, 1883 KB  
Article
A Hybrid Predictive Model for Employee Turnover: Integrating Ensemble Learning and Feature-Driven Insights from IBM HR Analytics
by Muna I. Alyousef, Hamza Wazir Khan and Mian Usman Sattar
Information 2026, 17(2), 208; https://doi.org/10.3390/info17020208 - 17 Feb 2026
Cited by 2 | Viewed by 1042
Abstract
Employee turnover presents a significant challenge to modern organizations, often resulting in operational disruptions, substantial hiring costs, and a loss of institutional knowledge. While traditional human resource practices have historically been reactive, the emergence of machine learning has introduced a proactive capability to [...] Read more.
Employee turnover presents a significant challenge to modern organizations, often resulting in operational disruptions, substantial hiring costs, and a loss of institutional knowledge. While traditional human resource practices have historically been reactive, the emergence of machine learning has introduced a proactive capability to anticipate and mitigate attrition before it occurs. This research utilizes the IBM HR Analytics dataset, which contains 1470 employee records and 35 distinct features, to develop a hybrid machine learning model designed to enhance the accuracy of turnover predictions. To ensure the model’s effectiveness, the researchers employed a comprehensive preprocessing phase that included eliminating non-informative features, applying label encoding to categorical data, and using StandardScaler to normalize quantitative values. A critical component of the study addressed the common issue of class imbalance within HR data. To resolve this, a hybrid sampling strategy was implemented, combining Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) to create a more balanced learning environment for the algorithms. The core of the predictive engine is a soft voting ensemble that integrates three powerful algorithms: Random Forest, XGBoost, and logistic regression. Evaluated on an 80/20 train–test split, the tuned XGBoost model achieved an impressive 84% accuracy and an Area Under the Curve (AUC) of 0.80. Meanwhile, the logistic regression component contributed the highest F1-score, reinforcing the overall strength and balance of the ensemble approach. These metrics confirm that the hybrid model is both robust and reliable for identifying at-risk employees. Beyond simple prediction, the study prioritized interpretability by using SHapley Additive exPlanations (SHAP) to identify the primary drivers of attrition. The analysis revealed that the most significant variables influencing an employee’s decision to leave include the interaction between job level and experience, frequent overtime, monthly income, current job level, and total years spent at the company. By providing these data-driven insights, the model empowers HR teams to transition from reactive troubleshooting to proactive retention planning, ultimately securing the organization’s talent and stability. Full article
(This article belongs to the Special Issue Machine Learning Approaches for Prediction and Decision Making)
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