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Symmetry
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Machine Learning and Data Analysis III
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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: 30 June 2026 | Viewed by 6593

Special Issue Editor

Dr. Marcin Michalak

E-Mail Website
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 250 words) can be sent to the Editorial Office for assessment.

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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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

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Research

17 pages, 4824 KB  
Article
An Improved Ensemble Learning Regression Algorithm for Electricity Demand Forecasting with Symmetric Experimental Evaluation
by Jie Zhou, Peisheng Yan, Zekang Bian, Zhibin Jiang and Donghua Yu
Symmetry 2026, 18(1), 123; https://doi.org/10.3390/sym18010123 - 8 Jan 2026
Viewed by 465
Abstract
Electricity demand forecasting plays a crucial role in energy planning and power system operation. However, it is affected by numerous factors and complex relationships, making accurate prediction challenging. Therefore, from the perspective of sample diversity in the base dataset, we propose an improved [...] Read more.
Electricity demand forecasting plays a crucial role in energy planning and power system operation. However, it is affected by numerous factors and complex relationships, making accurate prediction challenging. Therefore, from the perspective of sample diversity in the base dataset, we propose an improved stacking-based ensemble regression algorithm to enhance the accuracy of electricity demand forecasting. Firstly, a continuous sampling strategy is constructed between the sample integration selection probability and the base dataset using D2-Sampling and KNN; secondly, multiple base regression models are integrated through stacking to improve the predictive performance. In the electricity demand forecasting experiments conducted on three different datasets and across multiple base models, the proposed improved stacking ensemble learning regression algorithm (DK-Stacking) achieved the best performance. This symmetric experimental evaluation ensured consistent and balanced assessment of the model performance across datasets and models, highlighting the robustness and generalization of the proposed algorithm. Compared to the ANN, SVR, and RF models, its prediction accuracy increased by more than 1 percentage point. Even when compared to the optimized XGBoost model, it showed an improvement of 0.44 percentage points. Overall, the proposed DK-Stacking demonstrates symmetry-inspired robustness in electricity demand forecasting through the balanced treatment of datasets and model integration. Full article
(This article belongs to the Special Issue Machine Learning and Data Analysis III)
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31 pages, 14010 KB  
Article
Deep Reinforcement Learning for Financial Trading: Enhanced by Cluster Embedding and Zero-Shot Prediction
by Haoran Zhang, Xiaofei Li, Tianjiao Wan and Junjie Du
Symmetry 2026, 18(1), 112; https://doi.org/10.3390/sym18010112 - 7 Jan 2026
Viewed by 4529
Abstract
Deep reinforcement learning (DRL) plays a pivotal role in decision-making within financial markets. However, DRL models are highly reliant on raw market data and often overlook the impact of future trends on model performance. To address these challenges, we propose a novel framework [...] Read more.
Deep reinforcement learning (DRL) plays a pivotal role in decision-making within financial markets. However, DRL models are highly reliant on raw market data and often overlook the impact of future trends on model performance. To address these challenges, we propose a novel framework named Cluster Embedding-Proximal Policy Optimization (CE-PPO) for trading decision-making in financial markets. Specifically, the framework groups feature channels with intrinsic similarities and enhances the original model by leveraging clustering information instead of features from individual channels. Meanwhile, zero-shot prediction for unseen samples is achieved by assigning them to appropriate clusters. Future Open, High, Low, Close, and Volume (OHLCV) data predicted from observed values are integrated with actually observed OHLCV data, forming the state space inherent to reinforcement learning. Experiments conducted on five real-world financial datasets demonstrate that the time series model integrated with Cluster Embedding (CE) achieves significant improvements in predictive performance: in short-term prediction, the Mean Absolute Error (MAE) is reduced by an average of 20.09% and the Mean Squared Error (MSE) by 30.12%; for zero-shot prediction, the MAE and MSE decrease by an average of 21.56% and 31.71%, respectively. Through data augmentation using real and predicted data, the framework substantially enhances trading performance, achieving a cumulative return rate of 137.94% on the S&P 500 Index. Beyond its empirical contributions, this study also highlights the conceptual relevance of symmetry in the domain of algorithmic trading. The constructed deep reinforcement learning framework is capable of capturing the inherent balanced relationships and nonlinear interaction characteristics embedded in financial market behaviors. Full article
(This article belongs to the Special Issue Machine Learning and Data Analysis III)
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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 - 24 Oct 2025
Viewed by 1199
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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