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Applied Sciences
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Machine-Learning-Based Feature Extraction and Selection: 2nd Edition
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Machine-Learning-Based Feature Extraction and Selection: 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 2939

Special Issue Editor

Dr. David Ruano-Ordás

E-Mail Website
Guest Editor
1. CINBIO, Department of Computer Science, ESEI—Escuela Superior de Ingeniería Informática, University of Vigo, 32004 Ourense, Spain
2. SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
Interests: text mining; artificial intelligence; image processing machine learning; deep learning; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The technological advances attained during the last decade, together with the enhancement of data storage and computation capabilities, have stimulated the continuous generation and storage of large volumes of high-dimensional heterogeneous data at an unprecedented speed.

In this context, feature extraction and selection methods have become a crucial mechanism to alleviate the following two key issues related to high-dimensional data: (i) the increase in computational efforts required for its processing and/or analysis, and (ii) the existence of additional duplicated and/or meaningless information associated with the curse of dimensionality phenomenon.

In this Special Issue, we will explore the potential of applying machine learning-based feature extraction and selection methods to reduce model complexity by decreasing data dimensionality. This Special Issue is open for the publication of experimental works, properly validated designs, theoretical studies, and state-of-the-art review papers.

Dr. David Ruano-Ordás
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • information retrieval and text mining
  • machine learning
  • data mining and knowledge discovery
  • deep learning
  • information extraction
  • machine learning for NLP
  • dimensionality reduction

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Related Special Issue

Published Papers (2 papers)

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Research

20 pages, 1012 KiB  
Article
Analysing Social Media Discourse on Electric Vehicles with Machine Learning
by Yasin Özkara, Yasemin Bilişli, Fatih Serdar Yildirim, Fahrettin Kayan, Agah Başdeğirmen, Mehmet Kayakuş and Fatma Yiğit Açıkgöz
Appl. Sci. 2025, 15(8), 4395; https://doi.org/10.3390/app15084395 - 16 Apr 2025
Viewed by 343
Abstract
Social acceptance of electric vehicles is of great importance for environmental sustainability and economic development. This study aims to examine Turkish and English tweets about electric vehicles with sentiment analysis, text mining, and topic modelling techniques to reveal consumers’ electric vehicle purchasing behaviours, [...] Read more.
Social acceptance of electric vehicles is of great importance for environmental sustainability and economic development. This study aims to examine Turkish and English tweets about electric vehicles with sentiment analysis, text mining, and topic modelling techniques to reveal consumers’ electric vehicle purchasing behaviours, consumer perception and acceptance processes about electric vehicles, and social perceptions. The data was taken from the X platform, and high accuracy and F1 scores were obtained in both languages in the classification made with the deep learning-based LSTM model. The accuracy was 92.1% for English tweets and 96.7% for Turkish tweets. According to the sentiment analysis results, the perception of electric vehicles is generally positive in both languages. However, while the rate of neutral sentiment is higher in Turkish tweets, the rate of negative sentiment is higher in English tweets. This indicates that there is more criticism and debate about electric vehicles globally, while Turkish tweets have more neutral views on the subject. Word frequency analysis shows that positive comments about electric vehicles focus on economic and environmental advantages, while negative comments include concerns about charging time, battery life, and range concerns. The topic modelling identified three main themes related to electric vehicles: (1) reasons for being preferred by consumers and their purchasing tendencies, (2) the role of brands, (3) market developments and marketing strategies. In Turkish tweets, electric vehicle production, charging infrastructure, and consumer purchasing trends were at the forefront. In general, it is emphasised that charging infrastructure should be strengthened, battery performance should be improved, and costs should be reduced to accelerate the adoption of electric vehicles. Full article
(This article belongs to the Special Issue Machine-Learning-Based Feature Extraction and Selection: 2nd Edition)
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35 pages, 2051 KiB  
Article
Enhancing Agile Story Point Estimation: Integrating Deep Learning, Machine Learning, and Natural Language Processing with SBERT and Gradient Boosted Trees
by Burcu Yalçıner, Kıvanç Dinçer, Adil Gürsel Karaçor and Mehmet Önder Efe
Appl. Sci. 2024, 14(16), 7305; https://doi.org/10.3390/app14167305 - 19 Aug 2024
Cited by 3 | Viewed by 2232
Abstract
Advances in software engineering, particularly in Agile software development (ASD), demand innovative approaches to effort estimation due to the volatility in Agile environments. Recent trends have made the automation of story point (SP) estimation increasingly relevant, with significant potential for enhancing accuracy. This [...] Read more.
Advances in software engineering, particularly in Agile software development (ASD), demand innovative approaches to effort estimation due to the volatility in Agile environments. Recent trends have made the automation of story point (SP) estimation increasingly relevant, with significant potential for enhancing accuracy. This study introduces a novel model for software effort estimation (SEE) utilizing a deep learning (DL)-based sentence-BERT (SBERT) model for feature extraction combined with advanced gradient-boosted tree (GBT) algorithms. A comprehensive evaluation shows that the proposed model outperforms standard SEE and state-of-the-art models, demonstrating a mean absolute error (MAE) of 2.15 and a median absolute error (MdAE) of 1.85, representing a 12% improvement over the baseline model and an 18% improvement over the best-performing state-of-the-art model. The standardized accuracy (SA) is 93%, which is 7% higher than the next best model, across a dataset of 31,960 issues from 26 open-source Agile projects. This study contributes to software engineering by offering a more accurate and reliable decision support system for estimating project efforts. Full article
(This article belongs to the Special Issue Machine-Learning-Based Feature Extraction and Selection: 2nd Edition)
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