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Algorithms
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Algorithms for Feature Selection and Feature Reduction
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Algorithms for Feature Selection and Feature Reduction

A Special Issue of Algorithms (ISSN 1999-4893) belonging to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 1179

Editor

Dr. Artur Jorge Ferreira

E-Mail Website
Guest Editor
Lisbon School of Engineering, Polytechnic University of Lisbon, 1959-007 Lisboa, Portugal
Interests: artificial intelligence; bioinformatics; biometrics; data mining; deep learning; digital signal processing; evolutionary algorithms; feature discretization; feature selection; information theory; machine learning; neural networks; optimization algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Dimensionality reduction plays a key role in many pattern recognition and Machine Learning (ML) problems, improving results and the overall performance. The literature contains myriad dimensionality reduction techniques following diverse approaches for different problems. Despite the large number of sucessful approaches developed over the past decades, research continues to aim to further improve results in different kinds of problems.

Both Feature Selection (FS) and Feature Reduction (FR) are well-known Dimensionality Reduction (DR) techniques, and there are multiple successful approaches to both. The combination of both FS and FR within one DR method has also been addressed, with interesting results.

Recently, the use of nature-inspired, optimization, and evolutionary programming techniques, among others, has brought new insights into FS, with some studies now combining these Artificial Intelligence (AI)-based techniques with common ML approaches.

This Special Issue welcomes papers addressing scenarios and applications where the use of DR techniques plays a central role in improving the overall results. Both theoretical and application (real-world problems)-oriented papers are acceptable, with a slight preference towards approaches that combine AI with conventional DR approaches.

Dr. Artur Ferreira
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-anonymized 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

  • artificial intelligence
  • deep learning
  • dimensionality reduction
  • feature reduction
  • feature selection
  • hybrid methods
  • machine learning
  • optimization

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Published Papers (2 papers)

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Research

38 pages, 4961 KB  
Article
A Prior-Guided Structure-Aware Multi-Objective Differential Evolution Method for High-Dimensional Feature Selection
by Gaoxiang Huang, Jigen Luo, Ting Wang, Qiang Huang, Jia He, Huan Li, Zixuan Liu, Jiahe Cai and Jianqiang Du
Algorithms 2026, 19(9), 797; https://doi.org/10.3390/a19090797 - 17 Sep 2026
Viewed by 318
Abstract
Multi-objective feature selection provides an effective framework for high-dimensional data analysis by jointly considering classification performance and feature subset size. However, redundant and irrelevant features may degrade the quality of the initial search distribution, and population aggregation may reduce the structural diversity of [...] Read more.
Multi-objective feature selection provides an effective framework for high-dimensional data analysis by jointly considering classification performance and feature subset size. However, redundant and irrelevant features may degrade the quality of the initial search distribution, and population aggregation may reduce the structural diversity of non-dominated feature subsets. To address these issues, this paper proposes PGS-MODE-FS, a prior-guided and structure-aware multi-objective differential evolution method for high-dimensional feature selection. Specifically, feature–class relevance and feature redundancy are integrated into a unified feature importance measure to guide the generation of candidate solutions with different sparsity levels. The same feature-priority information is further used to construct a Top-k activated subspace, in which individuals are assigned to multiple islands according to their structural differences on informative features, thereby promoting diverse evolutionary search. Experiments on 17 public and biomedical datasets, including parameter analysis, comparative experiments, and ablation studies, demonstrate that PGS-MODE-FS achieves competitive performance in solution-set quality, classification accuracy, feature reduction, and computational efficiency. Further diversity analysis shows that the proposed multi-island mechanism effectively preserves structural diversity during evolution. Full article
(This article belongs to the Special Issue Algorithms for Feature Selection and Feature Reduction)
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26 pages, 1780 KB  
Article
A Hybrid ACO–Ensemble Learning Framework for Predicting Student Forum Consumption Behaviour
by Feziwe Lindiwe Yvonne Khomo and Richard Millham
Algorithms 2026, 19(9), 796; https://doi.org/10.3390/a19090796 - 17 Sep 2026
Viewed by 283
Abstract
Student engagement within Learning Management Systems (LMSs) provides valuable behavioural data for understanding and predicting learning outcomes. However, predicting students’ forum consumption behaviour remains challenging because LMS datasets may contain redundant engagement indicators that increase model complexity. This study proposes a hybrid predictive [...] Read more.
Student engagement within Learning Management Systems (LMSs) provides valuable behavioural data for understanding and predicting learning outcomes. However, predicting students’ forum consumption behaviour remains challenging because LMS datasets may contain redundant engagement indicators that increase model complexity. This study proposes a hybrid predictive modelling framework that integrates Ant Colony Optimisation (ACO) with three ensemble regression algorithms—Random Forest (RF), Gradient Boosting (GB), and Stacking—to predict forum consumption behaviour using LMS-derived engagement indicators. Guided by Educational Data Mining (EDM) and Social Learning Theory (SLT), behavioural, cognitive, and social engagement dimensions were operationalised using LMS indicators, with Freq_Forum_Consume serving as the target variable. ACO was employed as a wrapper-based feature-selection technique to identify informative predictors before model training. The performance of the ACO–ensemble models was compared with corresponding baseline models using the coefficient of determination (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results show that ACO reduced the predictor space from nine to six variables for GB and to seven variables for both Stacking and RF, while maintaining or improving predictive performance. ACO-GB achieved the strongest overall performance (R2 = 0.8332, MAE = 55.3173, RMSE = 71.4150). Consistent results across multiple ACO parameter configurations further demonstrated parameter consistency within the tested search settings. The selected predictors represented behavioural, cognitive, and social engagement dimensions, highlighting their complementary contribution to predicting forum consumption behaviour. The proposed framework provides a more parsimonious and interpretable approach to LMS-based learning analytics while retaining predictive performance. Full article
(This article belongs to the Special Issue Algorithms for Feature Selection and Feature Reduction)
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