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Algorithms
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Algorithms for Feature Selection (3rd Edition)
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Algorithms for Feature Selection (3rd Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 2324

Special Issue Editor

Dr. Muhammad Adnan Khan

E-Mail Website
Guest Editor
Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam 13120, Republic of Korea
Interests: algorithms; computational intelligence and its applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, feature selection has been acknowledged as a research field with significant activity due to the obvious emergence of datasets comprising large numbers of features. As a result, feature selection was considered an excellent technique for both improving the modeling of the underlying data-generation process and lowering the cost of obtaining the features. Additionally, from a machine learning perspective, because feature selection may shrink the complexity of an issue, it can be utilized to preserve or even boost the effectiveness of algorithms while minimizing computing costs. Recently, the emergence of big data has created new hurdles for machine learning researchers, who must now handle vast amounts of data, both in terms of instances and characteristics, rendering the learning process more complicated and computationally intensive than ever. While engaging with a significant number of features, the efficiency of learning algorithms might degrade due to overfitting; as learned models become increasingly complicated, their interpretability decreases, and the performance as well as efficacy of the algorithms are affected. Unfortunately, some of the most widely used algorithms were designed when dataset sizes were considerably smaller, and therefore do not scale well in the wake of these developments. Thus, it is necessary to repurpose these effective methods to address big data concerns.

For this Special Issue, we seek papers concerning current advances in feature selection algorithms for high-dimensional settings, as well as review papers that will motivate ongoing efforts to grasp the challenges commonly faced in this field. High-quality articles that address both theoretical and practical challenges related to feature selection algorithms are welcome.

Dr. Muhammad Adnan Khan
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.

Keywords

  • algorithms and techniques for feature selection based on evolutionary searches
  • ensemble methods for feature selection
  • feature selection for high-dimensional data
  • feature selection for time series data
  • feature selection applications
  • feature selection for textual data
  • deep feature selection

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Related Special Issue

Published Papers (3 papers)

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23 pages, 1983 KiB  
Article
Using Optimization Algorithms for Effective Missing-Data Imputation: A Case Study of Tabular Data Derived from Video Surveillance
by Mahmoud M. Eid, Kamal ElDahshan, Abdelatif H. Abouali and Alaa Tharwat
Algorithms 2025, 18(3), 119; https://doi.org/10.3390/a18030119 - 20 Feb 2025
Viewed by 524
Abstract
Data are crucial components of machine learning and deep learning in real-world applications. However, when collecting data from actual systems, we often encounter issues with missing information, which can harm accuracy and lead to biased results. In the context of video surveillance, missing [...] Read more.
Data are crucial components of machine learning and deep learning in real-world applications. However, when collecting data from actual systems, we often encounter issues with missing information, which can harm accuracy and lead to biased results. In the context of video surveillance, missing data may arise due to obstructions, varying camera angles, or technical issues, resulting in incomplete information about the observed scene. This paper introduces a method for handling missing data in tabular formats, specifically focusing on video surveillance. The core idea is to fill in the missing values for a specific feature using values from other related features rather than relying on all available features, which we refer to as the imputation approach based on informative features. The paper presents three sets of experiments. The first set uses synthetic datasets to compare four optimization algorithms—Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and the Sine–Cosine Algorithm (SCA)—to determine which one best identifies features related to the target feature. The second set works with real-world datasets, while the third focuses on video-surveillance datasets. Each experiment compares the proposed method, utilizing the best optimizer from the first set, against leading imputation methods. The experiments evaluate different types of data and various missing-data rates, ensuring that randomness does not introduce bias. In the first experiment, using only synthetic data, the results indicate that the WOA-based approach outperforms PSO, GWO, and SCA optimization algorithms. The second experiment used real datasets, while the third used tabular data extracted from a video-surveillance system. Both experiments show that our WOA-based imputation method produces promising results, outperforming other state-of-the-art imputation methods. Full article
(This article belongs to the Special Issue Algorithms for Feature Selection (3rd Edition))
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18 pages, 726 KiB  
Article
A Hybrid FMEA-ROC-CoCoSo Approach for Improved Risk Assessment and Reduced Complexity in Failure Mode Prioritization
by Vitor Anes and António Abreu
Algorithms 2024, 17(12), 585; https://doi.org/10.3390/a17120585 - 19 Dec 2024
Viewed by 747
Abstract
This paper proposes a novel hybrid model that integrates failure mode and effects analysis (FMEA), Rank Order Centroid (ROC), and Combined Compromise Solution (CoCoSo) to improve risk assessment and prioritization of failure modes. A case study in the healthcare sector will be conducted [...] Read more.
This paper proposes a novel hybrid model that integrates failure mode and effects analysis (FMEA), Rank Order Centroid (ROC), and Combined Compromise Solution (CoCoSo) to improve risk assessment and prioritization of failure modes. A case study in the healthcare sector will be conducted to validate the effectiveness of the proposed model. ROC is used to assign weights to the FMEA criteria (severity, occurrence, and detectability). CoCoSo is then applied to create a robust ranking of failure modes by considering multiple criteria simultaneously. The results of the case study show that the hybrid FMEA-ROC-CoCoSo model improves the accuracy and objectivity of risk prioritization. It effectively identifies critical failure modes, outperforming traditional FMEA. The hybrid approach not only improves risk management decision making, leading to better mitigation strategies and higher system reliability, but also reduces the complexity typically found in FMEA hybrid models. This model provides a more comprehensive risk assessment tool suitable for application in different industries. Full article
(This article belongs to the Special Issue Algorithms for Feature Selection (3rd Edition))
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22 pages, 1599 KiB  
Article
Single-Stage Entity–Relation Joint Extraction of Pesticide Registration Information Based on HT-BES Multi-Dimensional Labeling Strategy
by Chenyang Dong, Shiyu Xi, Yinchao Che, Shufeng Xiong, Xinming Ma, Lei Xi and Shuping Xiong
Algorithms 2024, 17(12), 559; https://doi.org/10.3390/a17120559 - 6 Dec 2024
Viewed by 625
Abstract
Pesticide registration information is an essential part of the pesticide knowledge base. However, the large amount of unstructured text data that it contains pose significant challenges for knowledge storage, retrieval, and utilization. To address the characteristics of pesticide registration text such as high [...] Read more.
Pesticide registration information is an essential part of the pesticide knowledge base. However, the large amount of unstructured text data that it contains pose significant challenges for knowledge storage, retrieval, and utilization. To address the characteristics of pesticide registration text such as high information density, complex logical structures, large spans between entities, and heterogeneous entity lengths, as well as to overcome the challenges faced when using traditional joint extraction methods, including triplet overlap, exposure bias, and redundant computation, we propose a single-stage entity–relation joint extraction model based on HT-BES multi-dimensional labeling (MD-SERel). First, in the encoding layer, to address the complex structural characteristics of pesticide registration texts, we employ RoBERTa combined with a multi-head self-attention mechanism to capture the deep semantic features of the text. Simultaneously, syntactic features are extracted using a syntactic dependency tree and graph neural networks to enhance the model’s understanding of text structure. Subsequently, we integrate semantic and syntactic features, enriching the character vector representations and thus improving the model’s ability to represent complex textual data. Secondly, in the multi-dimensional labeling framework layer, we use HT-BES multi-dimensional labeling, where the model assigns multiple labels to each character. These labels include entity boundaries, positions, and head–tail entity association information, which naturally resolves overlapping triplets. Through utilizing a parallel scoring function and fine-grained classification components, the joint extraction of entities and relations is transformed into a multi-label sequence labeling task based on relation dimensions. This process does not involve interdependent steps, thus enabling single-stage parallel labeling, preventing exposure bias and reducing computational redundancy. Finally, in the decoding layer, entity–relation triplets are decoded based on the predicted labels from the fine-grained classification. The experimental results demonstrate that the MD-SERel model performs well on both the Pesticide Registration Dataset (PRD) and the general DuIE dataset. On the PRD, compared to the optimal baseline model, the training time is 1.2 times faster, the inference time is 1.2 times faster, and the F1 score is improved by 1.5%, demonstrating its knowledge extraction capabilities in pesticide registration documents. On the DuIE dataset, the MD-SERel model also achieved better results compared to the baseline, demonstrating its strong generalization ability. These findings will provide technical support for the construction of pesticide knowledge bases. Full article
(This article belongs to the Special Issue Algorithms for Feature Selection (3rd Edition))
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