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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Izonin, Ivan
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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.
Co-Authors Gregus, Michal Korzh, Roman Kustra, Natalya Mitoulis, Stergios-Aristoteles Muzyka, Roman Tkachenko, Roman Yemets, Kyrylo