Applications of Advanced Machine Learning and Intelligent Data Analysis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 649

Special Issue Editor


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Guest Editor
School of Computer Science and Technology, Tianjing University, Tianjing, China
Interests: big; machine learning; data mining

Special Issue Information

Dear Colleagues,

This Special Issue explores the latest advancements in machine learning and intelligent data analysis, highlighting innovative methodologies, algorithms, and applications across various fields. It features a collection of research articles that delve into cutting-edge techniques such as deep, reinforcement, and unsupervised learning, emphasizing their effectiveness in solving complex real-world problems. Contributions include studies on the integration of machine learning with big data analytics, enhancing predictive modeling, and improving decision-making processes. This issue also examines the role of machine learning in healthcare, finance, and smart cities, showcasing practical implementations driving efficiency and innovation. This Special Issue aims to foster interdisciplinary collaboration and inspire future research directions in the realm of advanced machine learning and intelligent data analysis. We invite researcher to engage with the diverse methodologies and applications presented, as they represent the forefront of research in this dynamic field.

Dr. Yajun Yang
Guest Editor

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Keywords

  • machine learning
  • data analysis
  • deep learning
  • reinforcement learning
  • unsupervised learning
  • big data analysis
  • real-world applications

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Published Papers (1 paper)

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Research

26 pages, 3631 KiB  
Article
Ordinal Random Tree with Rank-Oriented Feature Selection (ORT-ROFS): A Novel Approach for the Prediction of Road Traffic Accident Severity
by Bita Ghasemkhani, Kadriye Filiz Balbal, Kokten Ulas Birant and Derya Birant
Mathematics 2025, 13(2), 310; https://doi.org/10.3390/math13020310 - 18 Jan 2025
Viewed by 522
Abstract
Road traffic accident severity prediction is crucial for implementing effective safety measures and proactive traffic management strategies. Existing methods often treat this as a nominal classification problem and use traditional feature selection techniques. However, ordinal classification methods that account for the ordered nature [...] Read more.
Road traffic accident severity prediction is crucial for implementing effective safety measures and proactive traffic management strategies. Existing methods often treat this as a nominal classification problem and use traditional feature selection techniques. However, ordinal classification methods that account for the ordered nature of accident severity (e.g., slight < serious < fatal injuries) in feature selection still need to be investigated thoroughly. In this study, we propose a novel approach, the Ordinal Random Tree with Rank-Oriented Feature Selection (ORT-ROFS), which utilizes the inherent ordering of class labels both in the feature selection and prediction stages for accident severity classification. The proposed approach enhances the model performance by separately determining feature importance based on severity levels. The experiments demonstrated the effectiveness of ORT-ROFS with an accuracy of 87.19%. According to the results, the proposed method improved prediction accuracy by 10.81% over state-of-the-art studies on average on different train–test split ratios. In addition, it achieved an average improvement of 4.58% in accuracy over traditional methods. These findings suggest that ORT-ROFS is a promising approach for accurate accident severity prediction, supporting road safety planning and intervention strategies. Full article
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