An Adaptation of the Input Doubling Method for Solving Classification Tasks in Case of Small Data Processing

Izonin, Ivan; Tkachenko, Roman; Havryliuk, Myroslav; Gregus, Michal; Yendyk, Pavlo; Tolstyak, Yaroslav

doi:10.1016/j.procs.2024.08.024

Procedia Computer Science

2024

DOI: 10.1016/j.procs.2024.08.024

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An Adaptation of the Input Doubling Method for Solving Classification Tasks in Case of Small Data Processing

Ivan Izonin,

Roman Tkachenko,

Myroslav Havryliuk

et al.

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2024

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Enhanced Input-Doubling Method Leveraging Response Surface Linearization to Improve Classification Accuracy in Small Medical Data Processing

Izonin,

Tkachenko,

Yendyk

et al. 2024

Computation

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Currently, the tasks of intelligent data analysis in medicine are becoming increasingly common. Existing artificial intelligence tools provide high effectiveness in solving these tasks when analyzing sufficiently large datasets. However, when there is very little training data available, current machine learning methods do not ensure adequate classification accuracy or may even produce inadequate results. This paper presents an enhanced input-doubling method for classification tasks in the case of limited data analysis, achieved via expanding the number of independent attributes in the augmented dataset with probabilities of belonging to each class of the task. The authors have developed an algorithmic implementation of the improved method using two Naïve Bayes classifiers. The method was modeled on a small dataset for cardiovascular risk assessment. The authors explored two options for the combined use of Naïve Bayes classifiers at both stages of the method. It was found that using different methods at both stages potentially enhances the accuracy of the classification task. The results of the improved method were compared with a range of existing methods used for solving the task. It was demonstrated that the improved input-doubling method achieved the highest classification accuracy based on various performance indicators.

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Enhanced Input-Doubling Method Leveraging Response Surface Linearization to Improve Classification Accuracy in Small Medical Data Processing

Izonin,

Tkachenko,

Yendyk

et al. 2024

Computation

View full text Add to dashboard Cite

show abstract

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An Adaptation of the Input Doubling Method for Solving Classification Tasks in Case of Small Data Processing

Cited by 1 publication

References 22 publications

Enhanced Input-Doubling Method Leveraging Response Surface Linearization to Improve Classification Accuracy in Small Medical Data Processing

Enhanced Input-Doubling Method Leveraging Response Surface Linearization to Improve Classification Accuracy in Small Medical Data Processing

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