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To enhance the accuracy of analyzing short datasets, this paper proposes a novel ensemble learning method that utilizes a single the General Regression Neural Network (GRNN). The core idea behind this method is the synthesis of additional pairs of vectors with different signs around each current vector from the test sample. This is achieved by employing the method of random symmetric perturbations and averaging the prediction outputs for the current vector and all synthesized vectors in its vicinity. Implementing this approach leads to a significant increase in prediction accuracy for short datasets. It achieves error compensation for each pair of additional vectors with different signs and also for the overall prediction result of the current vector and all additional pairs of synthetic vectors created for it. The effectiveness of the proposed method is validated through modeling on a small real-world biomedical dataset, and the optimal parameters have been selected. Comparative analysis with existing GRNN-based methods demonstrates a substantial improvement in accuracy. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to decide the Sm-Co alloy's maximum energy product prediction task based on the boosting strategy of the ensemble of machine learning methods. Design/methodology/approach: This paper examines an ensemble-based approach to solving Sm-Co alloy's maximum energy product prediction task. Because classical machine learning methods sometimes do not supply acceptable precision when solving the regression problem, the authors investigated the boosting ML model, namely Gradient Boosting. Building a boosting model based on several weak submodels, each of which considers the errors of the prior ones, provides substantial growth in the accuracy of the problem-solving. The obtained result is confirmed using an actual data set collected by the authors. Findings: This work demonstrates the high efficiency of applying the ensemble strategy of machine learning to the applied problem of materials science. The experiments determined the highest accuracy of solving the forecast task for the maximum energy product of Sm-Co alloy formed on the boosting model of machine learning in comparison with classical methods of machine learning. Research limitations/implications: The boosting strategy of machine learning, in comparison with single algorithms of machine learning, requires much more computational and time resources to implement the learning process of the model. Practical implications: This work demonstrated the possibility of effectively solving Sm-Co alloy's maximum energy product prediction task using machine learning. The studied boosting model of machine learning for solving the problem provides high accuracy of prediction, which reveals several advantages of their use in solving issues applied to computational material science. Furthermore, using the Orange modelling environment provides a simple and intuitive interface for using the researched methods. The proposed approach to the forecast significantly reduces the time and resource costs associated with studying expensive rare earth metals (REM)-based ferromagnetic materials. Originality/value: The authors have collected and formed a set of data on predicting the maximum energy product of the Sm-Co alloy. We used machine learning tools to solve the task. As a result, the most increased forecasting precision based on the boosting model is demonstrated compared to classical machine learning methods. [ABSTRACT FROM AUTHOR]