Machine Learning and Data Analysis III

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 274

Special Issue Editor


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Guest Editor
Department of Computer Networks and Systems, Silesian University of Technology, 44-100 Gliwice, Poland
Interests: image processing; data mining; machine learning; pattern recognition; rough set theory; biclustering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the great success of our Special Issue "Machine Learning and Data Analysis", we decided to set up the third volume.

There is no need to convince anyone about the huge influence of theoretical models of machine learning or data analysis techniques on our present way of living. They influence many scientific disciplines including industry, medicine, transport, and many others. We may observe how different approaches are mixed to become a new and complete model: classifiers for image analysis as well as image pattern recognition algorithms for classification; neural networks for clustering, classification, or time series prediction; and feature selection and extraction algorithms for the preprocessing step of many of the above-mentioned applications.

The topics of the Special Issue include but are not limited to the following:

  • Supervised learning;
  • Unsupervised learning;
  • Time series analysis;
  • Descriptive analysis;
  • Biclustering;
  • Genetic algorithms;
  • ML and DM applications;
  • Artificial neural networks;
  • Deep learning;
  • Decision support systems;
  • Anomaly detection;
  • Image analysis;
  • Pattern recognition.

Dr. Marcin Michalak
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
  • data analysis
  • process modelling
  • time series prediction

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Related Special Issue

Published Papers (1 paper)

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Research

20 pages, 1982 KB  
Article
Bias Term for Outlier Detection and Robust Regression
by Felix Ndudim and Thanasak Mouktonglang
Symmetry 2025, 17(11), 1796; https://doi.org/10.3390/sym17111796 (registering DOI) - 24 Oct 2025
Viewed by 124
Abstract
Noisy data and outliers are among the main challenges in machine learning, as their presence in training data can significantly reduce model performance and generalization. Detecting and handling these anomalous samples is particularly difficult because it is hard to distinguish them from normal [...] Read more.
Noisy data and outliers are among the main challenges in machine learning, as their presence in training data can significantly reduce model performance and generalization. Detecting and handling these anomalous samples is particularly difficult because it is hard to distinguish them from normal data. In this study, we propose a novel bias-based method (BT-SVR) to detect outliers and noisy inputs. The method uses a bias term derived from pairwise relationships among data points, which captures structural information about input distances. Outliers and noisy samples typically produce near-zero bias responses, allowing them to be identified effectively. A root-mean-square (RMS) scoring mechanism is then applied to quantify the anomaly strength of each sample, enabling the impact of outliers to be underweighted before training. Experiments demonstrate that BT-SVR improves the performance of Support Vector Regression (SVR) and enhances its robustness against noisy and anomalous data. Full article
(This article belongs to the Special Issue Machine Learning and Data Analysis III)
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