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Mathematics
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Applied and Methodological Data Science
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Applied and Methodological Data Science

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5296

Special Issue Editor

Dr. David Delgado-Gómez

E-Mail Website
Guest Editor
Department of Statistics, Universidad Carlos III of Madrid, 28903 Madrid, Spain
Interests: data science; machine learning; statistical learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, data science is an emerging field that is attracting an enormous amount of interest from both academia and industry. This interdisciplinary field applies the analytical knowledge generated in the areas of statistics, mathematics and machine learning, supported by recent advances in computer science, to an area of interest. In other words, it combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. This combination has succeeded in solving problems in several areas, including computer vision, natural language and medicine. These successes have led to data science being called the sexiest job in the 21st century.

This Special Issue searches for novel theoretical developments, as well as interesting applications of data science. Among the methodological advances are the following:

  • Deep learning (GANs, reinforcement learning, transfer learning… );
  • Kernel methods;
  • Ensemble methods;
  • Tree-based techniques;
  • Discriminants;
  • Gaussian processes;
  • Bayesian methods;
  • Unsupervised techniques.

Interesting applications include, but are not limited to, the following:

  • Natural language processing;
  • Computer vision;
  • Finance;
  • Medicine;
  • Recommender systems;
  • Industry;
  • Sports;
  • Biometry

Dr. David Delgado-Gómez
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 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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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 science
  • machine learning
  • statistical learning
  • deep learning
  • kernel methods

Published Papers (4 papers)

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Research

24 pages, 959 KiB  
Article
Modelling of Functional Profiles and Explainable Shape Shifts Detection: An Approach Combining the Notion of the Fréchet Mean with the Shape-Invariant Model
by Georgios I. Papayiannis, Stelios Psarakis and Athanasios N. Yannacopoulos
Mathematics 2023, 11(21), 4466; https://doi.org/10.3390/math11214466 - 28 Oct 2023
Cited by 1 | Viewed by 562
Abstract
A modelling framework suitable for detecting shape shifts in functional profiles combining the notion of the Fréchet mean and the concept of deformation models is developed and proposed. The generalized mean sense offered by the Fréchet mean notion is employed to capture the [...] Read more.
A modelling framework suitable for detecting shape shifts in functional profiles combining the notion of the Fréchet mean and the concept of deformation models is developed and proposed. The generalized mean sense offered by the Fréchet mean notion is employed to capture the typical pattern of the profiles under study, while the concept of deformation models, and in particular of the shape-invariant model, allows for interpretable parameterizations of the profile’s deviations from the typical shape. The EWMA-type control charts compatible with the functional nature of data and the employed deformation model are built and proposed, exploiting certain shape characteristics of the profiles under study with respect to the generalized mean sense, allowing for the identification of potential shifts concerning the shape and/or the deformation process. Potential shifts in the shape deformation process are further distinguished into significant shifts with respect to amplitude and/or the phase of the profile under study. The proposed modeling and shift detection framework is implemented to a real-world case study, where daily concentration profiles concerning air pollutants from an area in the city of Athens are modeled, while profiles indicating hazardous concentration levels are successfully identified in most cases. Full article
(This article belongs to the Special Issue Applied and Methodological Data Science)
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17 pages, 4056 KiB  
Article
Bisecting for Selecting: Using a Laplacian Eigenmaps Clustering Approach to Create the New European Football Super League
by Alexander John Bond and Clive B. Beggs
Mathematics 2023, 11(3), 720; https://doi.org/10.3390/math11030720 - 31 Jan 2023
Cited by 1 | Viewed by 1634
Abstract
Ranking sports teams generally relies on supervised techniques, requiring either prior knowledge or arbitrary metrics. In this paper, we offer a purely unsupervised technique. We apply this to operational decision-making, specifically, the controversial European Super League for association football, demonstrating how this approach [...] Read more.
Ranking sports teams generally relies on supervised techniques, requiring either prior knowledge or arbitrary metrics. In this paper, we offer a purely unsupervised technique. We apply this to operational decision-making, specifically, the controversial European Super League for association football, demonstrating how this approach can select dominant teams to form the new league. We first use random forest regression to select important variables predicting goal difference, which we use to calculate the Euclidian distances between teams. Creating a Laplacian eigenmap, we bisect the Fiedler vector to identify the natural clusters in five major European football leagues. Our results show how an unsupervised approach could identify four clusters based on five basic performance metrics: shots, shots on target, shots conceded, possession, and pass success. The top two clusters identify teams that dominate their respective leagues and are the best candidates to create the most competitive elite super league. Full article
(This article belongs to the Special Issue Applied and Methodological Data Science)
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19 pages, 1687 KiB  
Article
Feature Selection Based on Mud Ring Algorithm for Improving Survival Prediction of Children Undergoing Hematopoietic Stem-Cell Transplantation
by Lamiaa M. El Bakrawy, Nadjem Bailek, Laith Abualigah, Shabana Urooj and Abeer S. Desuky
Mathematics 2022, 10(22), 4197; https://doi.org/10.3390/math10224197 - 9 Nov 2022
Cited by 4 | Viewed by 1343
Abstract
The survival prediction of children undergoing hematopoietic stem-cell transplantation is essential for successful transplantation. However, the performance of current algorithms for predicting mortality in this patient group has not improved over recent decades. This paper proposes a new feature selection technique for survival [...] Read more.
The survival prediction of children undergoing hematopoietic stem-cell transplantation is essential for successful transplantation. However, the performance of current algorithms for predicting mortality in this patient group has not improved over recent decades. This paper proposes a new feature selection technique for survival prediction problems using the Mud Ring Algorithm (MRA). Experiments and tests were initially performed on 13 real datasets with varying occurrences to compare the suggested algorithm with other algorithms. After that, the constructed model classification performance was compared to other techniques using the bone marrow transplant children’s dataset. Modern techniques were used to acquire their classification results, which were then compared to the suggested outcomes using a variety of well-known metrics, graphical tools, and diagnostic analysis. This investigation has demonstrated that our suggested approach is comparable and outperformed other methods in terms of results. In addition, the results showed that the constructed model enhanced prediction accuracy by up to 82.6% for test cases. Full article
(This article belongs to the Special Issue Applied and Methodological Data Science)
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18 pages, 2004 KiB  
Article
A Generalized Linear Joint Trained Framework for Semi-Supervised Learning of Sparse Features
by Juan Carlos Laria, Line H. Clemmensen, Bjarne K. Ersbøll and David Delgado-Gómez
Mathematics 2022, 10(16), 3001; https://doi.org/10.3390/math10163001 - 19 Aug 2022
Cited by 1 | Viewed by 1146
Abstract
The elastic net is among the most widely used types of regularization algorithms, commonly associated with the problem of supervised generalized linear model estimation via penalized maximum likelihood. Its attractive properties, originated from a combination of ℓ1 and ℓ2 norms, endow [...] Read more.
The elastic net is among the most widely used types of regularization algorithms, commonly associated with the problem of supervised generalized linear model estimation via penalized maximum likelihood. Its attractive properties, originated from a combination of ℓ1 and ℓ2 norms, endow this method with the ability to select variables, taking into account the correlations between them. In the last few years, semi-supervised approaches that use both labeled and unlabeled data have become an important component in statistical research. Despite this interest, few researchers have investigated semi-supervised elastic net extensions. This paper introduces a novel solution for semi-supervised learning of sparse features in the context of generalized linear model estimation: the generalized semi-supervised elastic net (s2net), which extends the supervised elastic net method, with a general mathematical formulation that covers, but is not limited to, both regression and classification problems. In addition, a flexible and fast implementation for s2net is provided. Its advantages are illustrated in different experiments using real and synthetic data sets. They show how s2net improves the performance of other techniques that have been proposed for both supervised and semi-supervised learning. Full article
(This article belongs to the Special Issue Applied and Methodological Data Science)
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