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Algorithms
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Algorithms in Data Classification (3rd Edition)
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Algorithms in Data Classification (3rd Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Databases and Data Structures".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 5318

Special Issue Editor

Prof. Dr. Ioannis Tsoulos

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of Ioannina, 45110 Ioannina, Greece
Interests: optimization; neural networks; genetic algorithms; genetic programming
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I am pleased to invite submissions to the MDPI journal Algorithms for the forthcoming Special Issue entitled “Algorithms in Data Classification”. With this Special Issue, we aim to showcase recent advancements in the field of data classification and demonstrate their practical applications in solving real-world problems.

We welcome submissions focusing on the various methods employed in classification, including but not limited to Bayes methods, stochastic gradient descent, K-NN, decision trees, support vector machines, and neural networks. Furthermore, we encourage authors to explore the application of data classification in areas such as sentiment analysis, spam classification, document classification, and image classification.

This Special Issue presents a unique opportunity to contribute to the ever-evolving field of data classification and its real-world implications, and your expertise and research can make a constructive contribution to enriching the knowledge base and fostering advancements in this dynamic domain.

We invite you to submit your original research articles, literature reviews, or methodology papers to this Special Issue. We aim to gather a well-rounded collection of high-quality manuscripts that will serve as a valuable resource for both academia and industry. Appropriate topics include but are not limited to the following:

  • Binary classification;
  • Multi-class classification;
  • Multi-label classification;
  • Imbalanced classification;
  • Feature selection for classification;
  • Probabilistic models for classification;
  • Big data classification;
  • Text classification;
  • Multimedia classification;
  • Uncertain data classification.

Prof. Dr. Ioannis Tsoulos
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 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. 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 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

  • binary classification
  • multi-label classification
  • decision trees
  • neural networks
  • big data
  • Bayes methods
  • K-NN methods
  • feature selection
  • machine learning
  • supervised learning

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issues

Published Papers (5 papers)

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Research

14 pages, 324 KB  
Article
Improved Nonparallel Support Vector Machine for Pattern Classification
by Shujun Lian and Jingjing Yang
Algorithms 2026, 19(2), 124; https://doi.org/10.3390/a19020124 - 3 Feb 2026
Viewed by 207
Abstract
In this paper, we propose a new nonparallel support vector machine for binary classification problems and name it the improved nonparallel support vector machine (IMNSVM). The IMNSVM uses a one-sided ε-band and minimizes ε to achieve a better fitting effect for the [...] Read more.
In this paper, we propose a new nonparallel support vector machine for binary classification problems and name it the improved nonparallel support vector machine (IMNSVM). The IMNSVM uses a one-sided ε-band and minimizes ε to achieve a better fitting effect for the same class of training points. By introducing a new variable, ρ, the IMNSVM keeps one class of training points at a certain distance from the hyperplane corresponding to another class of training points, keeping them as far away as possible so as to better adapt to the training points and better describe the difference in data distribution between different categories. The IMNSVM can degenerate into the standard support vector machine (SVM) under certain conditions and is applicable to a wider range of data types. Finally, numerical experiments also explain the effectiveness of the method. Full article
(This article belongs to the Special Issue Algorithms in Data Classification (3rd Edition))
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19 pages, 376 KB  
Article
Net Rural Migration Classification in Colombia Using Supervised Decision Tree Algorithms
by Juan M. Sánchez, Helbert E. Espitia and Cesar L. González
Algorithms 2025, 18(12), 797; https://doi.org/10.3390/a18120797 - 16 Dec 2025
Viewed by 375
Abstract
This study presents a decision tree model-based approach to classify rural net migration across Colombian departments using sociodemographic and economic variables. In the model formulation, immigration is considered the movement of people to a destination area to settle there, while emigration is the [...] Read more.
This study presents a decision tree model-based approach to classify rural net migration across Colombian departments using sociodemographic and economic variables. In the model formulation, immigration is considered the movement of people to a destination area to settle there, while emigration is the movement of people from that specific area to other places. The target variable was defined as a binary category representing positive (when the immigration is greater than emigration) or negative net migration. Four classification models were trained and evaluated: Decision Tree, Random Forest, AdaBoost, and XGBoost. Data were preprocessed using cleaning techniques, categorical variable encoding, and class balance assessment. Model performance was evaluated using various metrics, including accuracy, precision, sensitivity, F1 score, and the area under the ROC curve. The results show that Random Forest achieves the highest accuracy, precision, sensitivity, and F1 score in the 10-variable and 15-variable settings, while XGBoost is competitive but not dominant. Furthermore, the importance of the model was analyzed to identify key factors influencing migration patterns. This approach allows for a more precise understanding of regional migration dynamics in Colombia and can serve as a basis for designing informed public policies. Full article
(This article belongs to the Special Issue Algorithms in Data Classification (3rd Edition))
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61 pages, 15479 KB  
Article
Feature Selection Method Based on Simultaneous Perturbation Stochastic Approximation Technique Evaluated on Cancer Genome Data Classification
by Satya Dev Pasupuleti and Simone A. Ludwig
Algorithms 2025, 18(10), 622; https://doi.org/10.3390/a18100622 - 1 Oct 2025
Viewed by 803
Abstract
Cancer classification using high-dimensional genomic data presents significant challenges in feature selection, particularly when dealing with datasets containing tens of thousands of features. This study presents a new application of the Simultaneous Perturbation Stochastic Approximation (SPSA) method for feature selection on large-scale cancer [...] Read more.
Cancer classification using high-dimensional genomic data presents significant challenges in feature selection, particularly when dealing with datasets containing tens of thousands of features. This study presents a new application of the Simultaneous Perturbation Stochastic Approximation (SPSA) method for feature selection on large-scale cancer datasets, representing the first investigation of the SPSA-based feature selection technique applied to cancer datasets of this magnitude. Our research extends beyond traditional SPSA applications, which have historically been limited to smaller datasets, by evaluating its effectiveness on datasets containing 35,924 to 44,894 features. Building upon established feature-ranking methodologies, we introduce a comprehensive evaluation framework that examines the impact of varying proportions of top-ranked features (5%, 10%, and 15%) on classification performance. This systematic approach enables the identification of optimal feature subsets most relevant to cancer detection across different selection thresholds. The key contributions of this work include the following: (1) the first application of SPSA-based feature selection to large-scale cancer datasets exceeding 35,000 features, (2) an evaluation methodology examining multiple feature proportion thresholds to optimize classification performance, (3) comprehensive experimental validation through comparison with ten state-of-the-art feature selection and classification methods, and (4) statistical significance testing to quantify the improvements achieved by the SPSA approach over benchmark methods. Our experimental evaluation demonstrates the effectiveness of the feature selection and ranking-based SPSA method in handling high-dimensional cancer data, providing insights into optimal feature selection strategies for genomic classification tasks. Full article
(This article belongs to the Special Issue Algorithms in Data Classification (3rd Edition))
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24 pages, 842 KB  
Article
Predicting the Magnitude of Earthquakes Using Grammatical Evolution
by Constantina Kopitsa, Ioannis G. Tsoulos and Vasileios Charilogis
Algorithms 2025, 18(7), 405; https://doi.org/10.3390/a18070405 - 1 Jul 2025
Cited by 1 | Viewed by 1757
Abstract
Throughout history, human societies have sought to explain natural phenomena through the lens of mythology. Earthquakes, as sudden and often devastating events, have inspired a range of symbolic and mythological interpretations across different civilizations. It was not until the 18th and 19th centuries [...] Read more.
Throughout history, human societies have sought to explain natural phenomena through the lens of mythology. Earthquakes, as sudden and often devastating events, have inspired a range of symbolic and mythological interpretations across different civilizations. It was not until the 18th and 19th centuries that a more positivist and scientific approach began to emerge regarding the explanation of earthquakes, recognizing their origin as stemming from processes occurring beneath the Earth’s surface. A pivotal moment in the emergence of modern seismology was the Lisbon earthquake of 1755, which marked a significant shift towards scientific inquiry. This means that the question of how earthquakes occur has been resolved; thanks to advancements in scientific, geological, and geophysical research, it is now well understood that seismic events result from the collision and movement of lithospheric or tectonic plates. The contemporary challenge that emerges, however, lies in whether such seismic phenomena can be accurately predicted. In this paper, a systematic attempt is made to use techniques based on Grammatical Evolution to determine the magnitude of earthquakes. These techniques use freely available data in which the history of large earthquakes is introduced before the application of the proposed techniques. From the execution of the experiments, it has become clear that the use of these techniques can allow for more effective estimation of the magnitude of earthquakes compared to other machine learning techniques from the relevant literature. Full article
(This article belongs to the Special Issue Algorithms in Data Classification (3rd Edition))
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18 pages, 3202 KB  
Article
DScanNet: Packaging Defect Detection Algorithm Based on Selective State Space Models
by Yirong Luo, Yanping Du, Zhaohua Wang, Jingtian Mo, Wenxuan Yu and Shuihai Dou
Algorithms 2025, 18(6), 370; https://doi.org/10.3390/a18060370 - 19 Jun 2025
Viewed by 1517
Abstract
With the rapid development of e-commerce and the logistics industry, the importance of logistics packaging defect detection as a key link in product quality control is becoming increasingly prominent. However, existing target detection models often face the problems of difficulty in improving detection [...] Read more.
With the rapid development of e-commerce and the logistics industry, the importance of logistics packaging defect detection as a key link in product quality control is becoming increasingly prominent. However, existing target detection models often face the problems of difficulty in improving detection accuracy and high model complexity when dealing with small-scale targets in logistics packaging. For this reason, an improved target detection model, DScanNet, is proposed in this paper. To address the problem that the model’s detailed feature extraction for small target defects is not sufficient and thus leads to low detection accuracy, the MEFE module, the local feature extraction module (LFEM Block), and the PCR module of the multi-scale convolution and feature enhancement strategy are proposed to enhance the model’s capability of capturing defective features and focusing on specific features, and to improve the detection accuracy. To address the problem of excessive model complexity, a Mamba module incorporating a channel attention mechanism is proposed to optimize the model via its linear complexity. Through experiments on its own dataset, BIGC-LP, DScanNet achieves a high accuracy of 96.8% on the defect detection task compared with the current mainstream detection algorithms, while the number of model parameters and the computational volume are effectively controlled. Full article
(This article belongs to the Special Issue Algorithms in Data Classification (3rd Edition))
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