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Symmetry
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Symmetry or Asymmetry in Machine Learning
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Symmetry or Asymmetry in Machine Learning

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 3390

Special Issue Editor

Dr. Shuang Xu

E-Mail Website
Guest Editor
School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710072, China
Interests: image resolution; geophysical image processing; image enhancement

Special Issue Information

Dear Colleagues,

The past decades have witnessed the fast growth of machine learning, with the rapid development of various techniques. When applying machine learning models to different fields, researchers and practitioners should pay attention to domain experience or prior knowledge, which may bring surprising gains and more insights. In particular, the symmetry/asymmetry property generally could play important roles in various problems and tasks, which potentially would inspire new models in machine learning.

This Special Issue mainly focus on novel machine learning models motivated by symmetry/asymmetry properties. The list of possible topics includes, but is not limited to, the following:

  • Supervised learning;
  • Unsupervised learning;
  • Computer vision and natural language processing;
  • Machine learning applications;
  • Deep learning and neural networks;
  • Pattern recognition;
  • Statistical modeling and inference.

Dr. Shuang Xu
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. Symmetry 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 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

  • machine learning
  • deep learning
  • computational statistics
  • artificial intelligence
  • data mining

Published Papers (2 papers)

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Research

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16 pages, 7820 KiB  
Article
Symmetrical and Asymmetrical Sampling Audit Evidence Using a Naive Bayes Classifier
by Guang-Yih Sheu and Nai-Ru Liu
Symmetry 2024, 16(4), 500; https://doi.org/10.3390/sym16040500 - 20 Apr 2024
Viewed by 278
Abstract
Taiwan’s auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target risker samples. We first classify [...] Read more.
Taiwan’s auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target risker samples. We first classify data using a Naive Bayes classifier into some classes. Next, a user-based, item-based, or hybrid approach is employed to draw audit evidence. The representativeness index is the primary metric for measuring its representativeness. The user-based approach samples data symmetrically around the median of a class as audit evidence. It may be equivalent to a combination of monetary and variable samplings. The item-based approach represents asymmetric sampling based on posterior probabilities for obtaining risky samples as audit evidence. It may be identical to a combination of non-statistical and monetary samplings. Auditors can hybridize those user-based and item-based approaches to balance representativeness and riskiness in selecting audit evidence. Three experiments show that sampling using machine learning integration has the benefits of drawing unbiased samples; handling complex patterns, correlations, and unstructured data; and improving efficiency in sampling big data. However, the limitations are the classification accuracy output by machine learning algorithms and the range of prior probabilities. Full article
(This article belongs to the Special Issue Symmetry or Asymmetry in Machine Learning)
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Review

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44 pages, 2174 KiB  
Review
A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective
by Mahnoor Chaudhry, Imran Shafi, Mahnoor Mahnoor, Debora Libertad Ramírez Vargas, Ernesto Bautista Thompson and Imran Ashraf
Symmetry 2023, 15(9), 1679; https://doi.org/10.3390/sym15091679 - 31 Aug 2023
Cited by 2 | Viewed by 2782
Abstract
Data mining is an analytical approach that contributes to achieving a solution to many problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable information from massive datasets. Clustering in data mining is used for splitting or segmenting data items/points into meaningful groups [...] Read more.
Data mining is an analytical approach that contributes to achieving a solution to many problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable information from massive datasets. Clustering in data mining is used for splitting or segmenting data items/points into meaningful groups and clusters by grouping the items that are near to each other based on certain statistics. This paper covers various elements of clustering, such as algorithmic methodologies, applications, clustering assessment measurement, and researcher-proposed enhancements with their impact on data mining thorough grasp of clustering algorithms, its applications, and the advances achieved in the existing literature. This study includes a literature search for papers published between 1995 and 2023, including conference and journal publications. The study begins by outlining fundamental clustering techniques along with algorithm improvements and emphasizing their advantages and limitations in comparison to other clustering algorithms. It investigates the evolution measures for clustering algorithms with an emphasis on metrics used to gauge clustering quality, such as the F-measure and the Rand Index. This study includes a variety of clustering-related topics, such as algorithmic approaches, practical applications, metrics for clustering evaluation, and researcher-proposed improvements. It addresses numerous methodologies offered to increase the convergence speed, resilience, and accuracy of clustering, such as initialization procedures, distance measures, and optimization strategies. The work concludes by emphasizing clustering as an active research area driven by the need to identify significant patterns and structures in data, enhance knowledge acquisition, and improve decision making across different domains. This study aims to contribute to the broader knowledge base of data mining practitioners and researchers, facilitating informed decision making and fostering advancements in the field through a thorough analysis of algorithmic enhancements, clustering assessment metrics, and optimization strategies. Full article
(This article belongs to the Special Issue Symmetry or Asymmetry in Machine Learning)
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