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Algorithms
Special Issues
Machine Learning Algorithms and Methods for Predictive Analytics
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Machine Learning Algorithms and Methods for Predictive Analytics

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 7973

Special Issue Editor

Dr. Shengkun Xie

E-Mail Website
Guest Editor
Global Management Studies, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON M5G 2C3, Canada
Interests: statistical machine learning; explainable data analytics; risk modeling; rate making; multivariate statistical methods; time series analysis; predictive analytics; health informatics; biosignal analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancements in machine learning algorithms and techniques have revolutionized the field of predictive analytics, becoming indispensable in real-world applications. The growing demand for predictive analytics underscores the critical need for specially designed machine learning algorithms that cater to the unique requirements of practitioners. These powerful algorithms possess the ability to extract valuable insights, patterns, and predictions from vast and complex datasets, thereby opening new avenues for decision making and problem solving across diverse domains, including science, engineering, business. Consequently, it is imperative for both academics and practitioners to explore and utilize these specially designed algorithms, while also considering their strengths and weaknesses, e.g., improved prediction accuracy, but lower level of interpretability. Therefore, achieving interpretability and explainability in machine learning models has emerged as a significant aspect of research.

In light of these developments, we are delighted to announce a new Special Issue entitled "Machine Learning Algorithms and Methods for Predictive Analytics". This Special Issue aims to bring together cutting-edge research contributions that delve into the latest advancements, challenges, and applications of machine learning algorithms and methods specifically tailored for predictive analytics. We cordially invite researchers, academics, and industry experts to contribute their original and innovative work in this exciting domain.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Supervised, unsupervised, and semi-supervised learning algorithms for predictive analytics.
  • Deep learning and neural network models for predictive analytics.
  • Feature selection and dimensionality reduction techniques for predictive modeling.
  • Ensemble methods and hybrid approaches for improved predictive performance.
  • Explainable and interpretable machine learning models for predictive analytics.
  • Handling imbalanced data and rare event prediction using machine learning.
  • Evaluation metrics and performance assessment for predictive analytics models.
  • Real-world applications of machine learning for predictive analytics, including healthcare, finance, economics, marketing, and more.

We welcome original research articles, reviews, and methodological papers that contribute to the advancement of machine learning algorithms and methods in the context of predictive analytics. All submissions will undergo a rigorous peer-review process to ensure the quality and relevance of accepted manuscripts.

Authors are kindly requested to follow the submission guidelines provided by the journal and submit their manuscripts electronically through the online submission system. The submitted papers should not have been published previously or be under consideration for publication elsewhere.

Dr. Shengkun Xie
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. Algorithms 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 1600 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.

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

23 pages, 2640 KiB  
Article
Improving Academic Advising in Engineering Education with Machine Learning Using a Real-World Dataset
by Mfowabo Maphosa, Wesley Doorsamy and Babu Paul
Algorithms 2024, 17(2), 85; https://doi.org/10.3390/a17020085 - 18 Feb 2024
Viewed by 2053
Abstract
The role of academic advising has been conducted by faculty-student advisors, who often have many students to advise quickly, making the process ineffective. The selection of the incorrect qualification increases the risk of dropping out, changing qualifications, or not finishing the qualification enrolled [...] Read more.
The role of academic advising has been conducted by faculty-student advisors, who often have many students to advise quickly, making the process ineffective. The selection of the incorrect qualification increases the risk of dropping out, changing qualifications, or not finishing the qualification enrolled in the minimum time. This study harnesses a real-world dataset comprising student records across four engineering disciplines from the 2016 and 2017 academic years at a public South African university. The study examines the relative importance of features in models for predicting student performance and determining whether students are better suited for extended or mainstream programmes. The study employs a three-step methodology, encompassing data pre-processing, feature importance selection, and model training with evaluation, to predict student performance by addressing issues such as dataset imbalance, biases, and ethical considerations. By relying exclusively on high school performance data, predictions are based solely on students’ abilities, fostering fairness and minimising biases in predictive tasks. The results show that removing demographic features like ethnicity or nationality reduces bias. The study’s findings also highlight the significance of the following features: mathematics, physical sciences, and admission point scores when predicting student performance. The models are evaluated, demonstrating their ability to provide accurate predictions. The study’s results highlight varying performance among models and their key contributions, underscoring the potential to transform academic advising and enhance student decision-making. These models can be incorporated into the academic advising recommender system, thereby improving the quality of academic guidance. Full article
(This article belongs to the Special Issue Machine Learning Algorithms and Methods for Predictive Analytics)
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17 pages, 4134 KiB  
Article
Predicting Decompensation Risk in Intensive Care Unit Patients Using Machine Learning
by Nosa Aikodon, Sandra Ortega-Martorell and Ivan Olier
Algorithms 2024, 17(1), 6; https://doi.org/10.3390/a17010006 - 22 Dec 2023
Viewed by 2669
Abstract
Patients in Intensive Care Units (ICU) face the threat of decompensation, a rapid decline in health associated with a high risk of death. This study focuses on creating and evaluating machine learning (ML) models to predict decompensation risk in ICU patients. It proposes [...] Read more.
Patients in Intensive Care Units (ICU) face the threat of decompensation, a rapid decline in health associated with a high risk of death. This study focuses on creating and evaluating machine learning (ML) models to predict decompensation risk in ICU patients. It proposes a novel approach using patient vitals and clinical data within a specified timeframe to forecast decompensation risk sequences. The study implemented and assessed long short-term memory (LSTM) and hybrid convolutional neural network (CNN)-LSTM architectures, along with traditional ML algorithms as baselines. Additionally, it introduced a novel decompensation score based on the predicted risk, validated through principal component analysis (PCA) and k-means analysis for risk stratification. The results showed that, with PPV = 0.80, NPV = 0.96 and AUC-ROC = 0.90, CNN-LSTM had the best performance when predicting decompensation risk sequences. The decompensation score’s effectiveness was also confirmed (PPV = 0.83 and NPV = 0.96). SHAP plots were generated for the overall model and two risk strata, illustrating variations in feature importance and their associations with the predicted risk. Notably, this study represents the first attempt to predict a sequence of decompensation risks rather than single events, a critical advancement given the challenge of early decompensation detection. Predicting a sequence facilitates early detection of increased decompensation risk and pace, potentially leading to saving more lives. Full article
(This article belongs to the Special Issue Machine Learning Algorithms and Methods for Predictive Analytics)
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19 pages, 10277 KiB  
Article
Learning to Extrapolate Using Continued Fractions: Predicting the Critical Temperature of Superconductor Materials
by Pablo Moscato, Mohammad Nazmul Haque, Kevin Huang, Julia Sloan and Jonathon Corrales de Oliveira
Algorithms 2023, 16(8), 382; https://doi.org/10.3390/a16080382 - 8 Aug 2023
Cited by 4 | Viewed by 2424
Abstract
In the field of Artificial Intelligence (AI) and Machine Learning (ML), a common objective is the approximation of unknown target functions y=f(x) using limited instances S=(x(i),y(i)) [...] Read more.
In the field of Artificial Intelligence (AI) and Machine Learning (ML), a common objective is the approximation of unknown target functions y=f(x) using limited instances S=(x(i),y(i)), where x(i)∈D and D represents the domain of interest. We refer to S as the training set and aim to identify a low-complexity mathematical model that can effectively approximate this target function for new instances x. Consequently, the model’s generalization ability is evaluated on a separate set T={x(j)}⊂D, where T≠S, frequently with T∩S=∅, to assess its performance beyond the training set. However, certain applications require accurate approximation not only within the original domain D but in an extended domain D′ that encompasses D as well. This becomes particularly relevant in scenarios involving the design of new structures, where minimizing errors in approximations is crucial. For example, when developing new materials through data-driven approaches, the AI/ML system can provide valuable insights to guide the design process by serving as a surrogate function. Consequently, the learned model can be employed to facilitate the design of new laboratory experiments. In this paper, we propose a method for multivariate regression based on iterative fitting of a continued fraction, incorporating additive spline models. We compare the performance of our method with established techniques, including AdaBoost, Kernel Ridge, Linear Regression, Lasso Lars, Linear Support Vector Regression, Multi-Layer Perceptrons, Random Forest, Stochastic Gradient Descent, and XGBoost. To evaluate these methods, we focus on an important problem in the field, namely, predicting the critical temperature of superconductors based on their physical–chemical characteristics. Full article
(This article belongs to the Special Issue Machine Learning Algorithms and Methods for Predictive Analytics)
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