Machine Learning and Data Mining: Theory and Applications

Topic Information

Dear Colleagues,

Recent decades have witnessed a rapid evolution of machine learning (ML) and data mining (DM) techniques, driven by advances in computing power, big data, and artificial intelligence. These technologies have transformed diverse domains such as healthcare, finance, manufacturing, transportation, and smart environments. This Topic aims to gather high-quality contributions that explore both the theoretical foundations and practical applications of ML and DM. We welcome research on emerging algorithms, optimization strategies, and innovative architectures, as well as studies addressing real-world challenges through predictive modeling, pattern recognition, and knowledge discovery. Particular attention will be given to works that integrate ML and DM with cutting-edge paradigms such as deep learning, IoT, cloud computing, and ethical AI.

Prof. Dr. Jonathan Cepeda-Negrete
Prof. Dr. Qianmu Li
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	2.1	4.5	2008	19.2 Days	CHF 1800	Submit
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
AppliedMath appliedmath	0.7	1.1	2021	20.6 Days	CHF 1200	Submit
Data data	2.0	5.0	2016	25 Days	CHF 1600	Submit
Information information	2.9	6.5	2010	20.9 Days	CHF 1800	Submit
Symmetry symmetry	2.2	5.3	2009	15.8 Days	CHF 2400	Submit

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All Algorithms Applied Sciences AppliedMath Data Information Symmetry

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20 pages, 1426 KB  

Open AccessReview

Profiling Decision-Making Styles Under Healthcare Resource Scarcity: An Interdisciplinary Clustering Approach

by Micaela Pinho, Fátima Leal and Isabel Miguel

Information 2026, 17(3), 287; https://doi.org/10.3390/info17030287 - 14 Mar 2026

Viewed by 322

Abstract

Scarcity of healthcare resources requires prioritisation decisions that raise complex ethical, economic, and social challenges. While normative frameworks provide guidance on how such decisions ought to be made, growing evidence suggests that individuals differ substantially in how they approach morally charged allocation choices. [...] Read more.

Scarcity of healthcare resources requires prioritisation decisions that raise complex ethical, economic, and social challenges. While normative frameworks provide guidance on how such decisions ought to be made, growing evidence suggests that individuals differ substantially in how they approach morally charged allocation choices. This study investigates heterogeneity in decision-making styles and support for healthcare prioritisation criteria using an interdisciplinary approach that integrates health economics, social psychology, and computational methods to identify latent decision-making profiles among a sample of adults residing in Portugal. Data were collected from adults residing in Portugal using a structured online questionnaire comprising socio-demographic characteristics, decision-making styles, and preferences elicited through twenty hypothetical healthcare rationing scenarios. The results reveal three meaningful decision-making profiles characterised by different combinations of cognitive styles and ethical prioritisation patterns: analytically oriented decision-makers prioritising health gains; intuitive, context-sensitive decision-makers balancing clinical and social criteria; heuristic-driven decision-makers relying on simpler or less differentiated heuristics. These findings demonstrate that, within this sample, healthcare prioritisation preferences are shaped by systematic variations in decision style rather than a single moral or rational framework. By linking behavioural heterogeneity with ethical decision-making, this study contributes to theoretical debates on healthcare rationing and demonstrates the value of clustering techniques for uncovering latent structures in complex decision data. The results provide insights relevant for the design of decision-support systems and rationing policies, which may be adapted to accommodate heterogeneous decision styles in comparable settings. Full article

(This article belongs to the Topic Machine Learning and Data Mining: Theory and Applications)

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25 pages, 639 KB  

Open AccessArticle

A Sparse ${\mathcal{L}}_{\infty }$-Norm Regularized Least Squares Support Vector Regression

by Xiaoyong Liu, Dong Li and Chengbin Zeng

Algorithms 2026, 19(2), 160; https://doi.org/10.3390/a19020160 - 18 Feb 2026

Viewed by 354

Abstract

Although Least Squares Support Vector Regression (LSSVR) reduces the hyperparameter space to two, it sacrifices sparsity, causing all training samples to become support vectors and increasing storage costs. In contrast, standard Support Vector Regression (SVR) preserves sparsity but requires tuning three highly coupled [...] Read more.

Although Least Squares Support Vector Regression (LSSVR) reduces the hyperparameter space to two, it sacrifices sparsity, causing all training samples to become support vectors and increasing storage costs. In contrast, standard Support Vector Regression (SVR) preserves sparsity but requires tuning three highly coupled hyperparameters, leading to higher computational burden. To address these limitations, this paper proposes a sparse ${\mathcal{L}}_{\infty }$-norm regularized least squares SVR framework that incorporates the infinity norm of approximation errors into both the objective function and inequality constraints. The resulting optimization problem minimizes model complexity while controlling the maximum prediction deviation through a single slack variable, thereby transforming the conventional three-hyperparameter SVR tuning task into a two-parameter problem involving only the regularization coefficient and kernel width. This formulation restores sparsity by enabling a compact support vector set, while preserving the stability and convexity advantages of LSSVR. Experiments on both static and dynamic datasets demonstrate that the proposed method consistently achieves higher predictive accuracy and improved robustness compared with standard SVR and LSSVR. These results indicate that the proposed ${\mathcal{L}}_{\infty }$-norm regularized framework offers a mathematically principled and computationally efficient alternative for sparse, robust, and scalable regression modeling. Full article

(This article belongs to the Topic Machine Learning and Data Mining: Theory and Applications)

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