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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Gregus, Michal
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Computer Science & IT Electrical & Electronics Engineering Engineering

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An approach toward improvement of ensemble method’s accuracy for biomedical data classification Izonin, Ivan; Muzyka, Roman; Tkachenko, Roman; Gregus, Michal; Kustra, Natalya; Mitoulis, Stergios-Aristoteles
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5949-5960

Abstract

Amidst rapid technological and healthcare advancements, biomedical data classification using machine learning (ML) is pivotal for revolutionizing medical diagnosis, treatment, and research by organizing vast healthcare-related data. Despite efforts to apply single ML models on clean datasets, satisfactory classification accuracy can still be elusive. In such cases, ML-based ensembles offer a promising solution. This paper explores cascaded ensembles as highly accurate methods. Existing cascade classifiers often partition large datasets into equal unique parts, limiting accuracy due to insufficient amount of useful information processed by weak classifiers of all levels of the cascade ensemble. To address this, we propose an improved cascaded ensemble scheme using a different data sampling approach. Our method forms larger subsamples at each cascade level, enhancing accuracy, and generalization properties during biomedical data analysis. Experimental comparisons demonstrate substantial increases in classification accuracy and generalization properties of the improved cascade ensemble.
An enhanced cascade ensemble method for big data analysis Izonin, Ivan; Muzyka, Roman; Tkachenko, Roman; Gregus, Michal; Korzh, Roman; Yemets, Kyrylo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp963-974

Abstract

In the digital age, the proliferation of data presents both challenges and opportunities, particularly in the realm of big data, which is characterized by its volume, velocity, and variety. Machine learning is a crucial technology for extracting insights from these vast datasets. Among machine learning methods, ensemble methods, and especially cascading ensembles, are highly effective for big data analysis. While it is true that the training procedures for cascade ensembles can be time-consuming and may have limitations in terms of accuracy, this paper proposes a solution to enhance their performance. Our method involves using stochastic gradient descent (SGD) classifiers, an improved training data separation algorithm, and integrating principal component analysis (PCA) at each ensemble level. We are confident that these enhancements lead to improved results and accuracy. The proposed approach is designed to enhance both the generalization properties and accuracy of the ensemble (3%), while also reducing its training time. Results from modelling on a real-world biomedical dataset demonstrate significant reductions in training duration, improvements in generalization properties, and enhanced accuracy when compared to other possible implementations of the ensemble.
Schedule-free optimization of the transformers-based time series forecasting model Yemets, Kyrylo; Greguš, Michal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1067-1076

Abstract

The task of time series forecasting is important for many scientific, technical, and applied fields, such as finance, economics, meteorology, medicine, transportation, and telecommunications. Existing methods, such as autoregressive models and moving average models, have their limitations, especially when working with non-stationary and seasonal data. In this work, the basic architecture of transformers was modified to solve time series forecasting problems. Additionally, state-of-the-art optimizers were investigated and experimentally compared, including AdamW, stochastic gradient descent (SGD), and new methods such as schedule-free SGD and schedule-free AdamW, to improve forecasting accuracy and the efficiency of the training procedure for the transformer architecture. Modeling was conducted on meteorological data that included seasonal time series. The accuracy evaluation of the optimization methods studied in this work was performed using a range of different performance indicators. The results showed that the new optimization methods significantly improve forecasting accuracy compared to the use of traditional optimizers.
A transformer-based time series forecasting model with an efficient data preprocessing scheme Yemets, Kyrylo; Gregus, Michal
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9280

Abstract

Time series forecasting with cyclicality is key to the development of green energy, particularly wind energy, due to its high volatility. Accurate forecasting allows for optimal use of energy storage systems and balancing of power grids. In this article, the authors have developed a model for forecasting time series in wind energy through the combined use of Fourier transform and an adapted transformer architecture to solve the time series forecasting problem. The use of Fourier transform provided the ability to detect and account for hidden periodicities that may not be obvious in simple time series analysis, and allowed for the separation of random fluctuations from significant cyclical components, contributing to more accurate data analysis. The use of transformer architecture made it possible to effectively account for both short-term fluctuations and long-term trends in wind patterns, creating more accurate and reliable forecasts of wind energy production. The results show that the model outperforms methods such as transformers, long short term memory (LSTM), LSTM with Fourier transform, and DeepAR in forecast accuracy, taking into account seasonal, weather, and daily cycles of wind data.
Co-Authors Izonin, Ivan Korzh, Roman Kustra, Natalya Mitoulis, Stergios-Aristoteles Muzyka, Roman Tkachenko, Roman Yemets, Kyrylo