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Applied Sciences
Special Issues
Machine-Learning-Based Feature Extraction and Selection
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Machine-Learning-Based Feature Extraction and Selection

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 July 2024 | Viewed by 7321

Special Issue Editor

Dr. David Ruano Ordás

E-Mail Website
Guest Editor
SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Department of Computer Science, Universidade de Vigo, ESEI—Escola Superior de Enxeñaría Informática, Edificio Politécnico, Campus Universitario As Lagoas S/N, 32004 Ourense, 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 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

Published Papers (4 papers)

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Research

16 pages, 1102 KiB  
Article
A New Permutation-Based Method for Ranking and Selecting Group Features in Multiclass Classification
by Iqbal Muhammad Zubair, Yung-Seop Lee and Byunghoon Kim
Appl. Sci. 2024, 14(8), 3156; https://doi.org/10.3390/app14083156 - 09 Apr 2024
Viewed by 344
Abstract
The selection of group features is a critical aspect in reducing model complexity by choosing the most essential group features, while eliminating the less significant ones. The existing group feature selection methods select a set of important group features, without providing the relative [...] Read more.
The selection of group features is a critical aspect in reducing model complexity by choosing the most essential group features, while eliminating the less significant ones. The existing group feature selection methods select a set of important group features, without providing the relative importance of all group features. Moreover, few methods consider the relative importance of group features in the selection process. This study introduces a permutation-based group feature selection approach specifically designed for high-dimensional multiclass datasets. Initially, the least absolute shrinkage and selection operator (lasso) method was applied to eliminate irrelevant individual features within each group feature. Subsequently, the relative importance of the group features was computed using a random-forest-based permutation method. Accordingly, the process selected the highly significant group features. The performance of the proposed method was evaluated using machine learning algorithms and compared with the performance of other approaches, such as group lasso. We used real-world, high-dimensional, multiclass microarray datasets to demonstrate its effectiveness. The results highlighted the capability of the proposed method, which not only selected significant group features but also provided the relative importance and ranking of all group features. Furthermore, the proposed method outperformed the existing method in terms of accuracy and F1 score. Full article
(This article belongs to the Special Issue Machine-Learning-Based Feature Extraction and Selection)
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15 pages, 6690 KiB  
Article
Application of Remote Sensing and Geographic Information System Technologies to Assess the Impact of Mining: A Case Study at Emalahleni
by Monica Naa Morkor Cudjoe, Efiba Vidda Senkyire Kwarteng, Enoch Anning, Idowu Racheal Bodunrin and Samuel Ato Andam-Akorful
Appl. Sci. 2024, 14(5), 1739; https://doi.org/10.3390/app14051739 - 21 Feb 2024
Viewed by 518
Abstract
This article presents an assessment of the impact of mining activities in the Emalahleni municipality, using GIS and RS technologies. The random forest algorithm was used to classify the land use and land cover in the Emalahleni municipality over a three-decade period (1990–2020). [...] Read more.
This article presents an assessment of the impact of mining activities in the Emalahleni municipality, using GIS and RS technologies. The random forest algorithm was used to classify the land use and land cover in the Emalahleni municipality over a three-decade period (1990–2020). The classifications are settlement, water, mining area, vegetation, and bare land. The majority of the study area was found to be rocky ground, accounting for approximately 60% of the total study area. Change detection maps were created for vegetation and mining to assess the extent of land degradation in the study area over the three-decade period. The findings in this study highlight the importance of understanding the changes in land use and vegetation cover in the study area and its impact on the environment, as well as the local community. It is crucial to develop sustainable land management strategies that ensure that a reasonable balance concerning the economic development activities is achieved, such as mining with environmental management for its long-term viability for future generations. The data presented in this study provides a useful baseline for further research and can inform land-use planning and decision-making processes in Emalahleni. Full article
(This article belongs to the Special Issue Machine-Learning-Based Feature Extraction and Selection)
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11 pages, 4484 KiB  
Article
A Hybrid CNN and RNN Variant Model for Music Classification
by Mohsin Ashraf, Fazeel Abid, Ikram Ud Din, Jawad Rasheed, Mirsat Yesiltepe, Sook Fern Yeo and Merve T. Ersoy
Appl. Sci. 2023, 13(3), 1476; https://doi.org/10.3390/app13031476 - 22 Jan 2023
Cited by 10 | Viewed by 3906
Abstract
Music genre classification has a significant role in information retrieval for the organization of growing collections of music. It is challenging to classify music with reliable accuracy. Many methods have utilized handcrafted features to identify unique patterns but are still unable to determine [...] Read more.
Music genre classification has a significant role in information retrieval for the organization of growing collections of music. It is challenging to classify music with reliable accuracy. Many methods have utilized handcrafted features to identify unique patterns but are still unable to determine the original music characteristics. Comparatively, music classification using deep learning models has been shown to be dynamic and effective. Among the many neural networks, the combination of a convolutional neural network (CNN) and variants of a recurrent neural network (RNN) has not been significantly considered. Additionally, addressing the flaws in the particular neural network classification model, this paper proposes a hybrid architecture of CNN and variants of RNN such as long short-term memory (LSTM), Bi-LSTM, gated recurrent unit (GRU), and Bi-GRU. We also compared the performance based on Mel-spectrogram and Mel-frequency cepstral coefficient (MFCC) features. Empirically, the proposed hybrid architecture of CNN and Bi-GRU using Mel-spectrogram achieved the best accuracy at 89.30%, whereas the hybridization of CNN and LSTM using MFCC achieved the best accuracy at 76.40%. Full article
(This article belongs to the Special Issue Machine-Learning-Based Feature Extraction and Selection)
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14 pages, 904 KiB  
Article
Malicious URL Detection Model Based on Bidirectional Gated Recurrent Unit and Attention Mechanism
by Tiefeng Wu, Miao Wang, Yunfang Xi and Zhichao Zhao
Appl. Sci. 2022, 12(23), 12367; https://doi.org/10.3390/app122312367 - 02 Dec 2022
Cited by 5 | Viewed by 1631
Abstract
With the rapid development of Internet technology, numerous malicious URLs have appeared, which bring a large number of security risks. Efficient detection of malicious URLs has become one of the keys for defense against cyber attacks. Deep learning methods bring new developments to [...] Read more.
With the rapid development of Internet technology, numerous malicious URLs have appeared, which bring a large number of security risks. Efficient detection of malicious URLs has become one of the keys for defense against cyber attacks. Deep learning methods bring new developments to the identification of malicious web pages. This paper proposes a malicious URL detection method based on a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The method is based on the BiGRU model. A regularization operation called a dropout mechanism is added to the input layer to prevent the model from overfitting, and an attention mechanism is added to the middle layer to strengthen the feature learning of URLs. Finally, the deep learning network DA-BiGRU model is formed. The experimental results demonstrate that the proposed method can achieve better classification results in malicious URL detection, which has high significance for practical applications. Full article
(This article belongs to the Special Issue Machine-Learning-Based Feature Extraction and Selection)
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