Analysis of Deep Learning Methods in Adaptation to the Small Data Problem Solving

Krak, Iurii; Kuznetsov, Vladyslav; Kondratiuk, Serhii; Аzarova, Larysa; Barmak, Olexander; Радюк, Павло

doi:10.1007/978-3-031-16203-9_20

Cited by 11 publications

(3 citation statements)

References 26 publications

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“…All this requires specialized approaches, methods, and an understanding of the specifics of the medical context to achieve reliable and valuable results when analyzing biomedical data sets of small volumes. That is why, to increase the accuracy of classification in biomedical engineering, new hybrid methods of data analysis are constantly being developed, which include machine learning, fuzzy logic [9], deep learning [10], kernel methods [11], and statistical approaches [12].…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised-Supervised Ensemble Technology With Non-Iterative Training Algorithm for Small Biomedical Data Analysis

IZONIN

2023

CSIT

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Improving the accuracy of intelligent data analysis is an important task in various application areas. Existing machine learning methods do not always provide a sufficient level of classification accuracy for their use in practice. That is why, in recent years, hybrid ensemble methods of intellectual data analysis have begun to develop. They are based on the combined use of clustering and classification procedures. This approach provides an increase in the accuracy of the classifier based on machine learning due to the expansion of the space of the input data of the task by the results of the clustering. In this paper, the tasks of modification and improvement of such technology for small data analysis are considered. The basis of the modification is the use of clustering with output at the first step of the method to increase the accuracy of the entire technology. Despite the high accuracy of the work, this approach requires a significant expansion of the inputs of the final linear classifier (labels of the obtained clusters are added to the initial inputs). To avoid this shortcoming, the paper proposes an improvement based on the introduction of a new classification procedure at the first step of the method and replaces all the initial inputs of the task with the results of its work. In parallel with it, clustering is performed taking into account the original attribute, the results of which are added to the output of the classifier of the first step. In this way, the formation of an expanded set of data of significantly lower dimensionality in comparison with the existing method takes place (here there is no longer a large number of initial features, which is characteristic of biomedical engineering tasks). This reduces the training time of the method and increases its generalization properties. Modeling of the method was based on the use of a short dataset contained in an open repository. After the preprocessing procedures, the dataset has only 294 vectors, each of which was characterized by 18 attributes. Data classification was done using an SGTM-based neural-like structure classifier. This linear classifier provides high accuracy of work. In addition, it does not provide for the implementation of an iterative training procedure and additional adjustment of work parameters. Data clustering was performed using the k-means method. This choice is due to both the simplicity and speed of its work. The search for the optimal number of k-means clusters was carried out using 4 different methods. They all showed different results. That is why, some experiments were conducted to assess the influence of different numbers of clusters (from 3 to 7) on the accuracy of all 4 algorithms of the developed technology. The accuracy of the proposed technology has been established experimentally in comparison with the linear classifier and the existing hybrid method. In addition, by reducing the inputs of the final classifier, the developed technology reduces the duration of the training procedure compared to the basic method. All this ensures the possibility of using the proposed technology when solving various applied problems of medical diagnostics, in particular, based on the analysis of small data. Keywords: small data approach, non-iterative training, ensemble learning, unsupervised-supervised technology, biomedical engineering.

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Section: Introductionmentioning

confidence: 99%

An Unsupervised-Supervised Ensemble Technology With Non-Iterative Training Algorithm for Small Biomedical Data Analysis

IZONIN

2023

CSIT

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“…dataset to increase the effectiveness of the training procedure by the chosen method [11]. In this paper, we use other approaches.…”

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confidence: 99%

An Ensemble Method for the Analysis of Small Biomedical Data based on a Neural Network Without Training

Izonin,

Tkachenko,

Semchyshyn

2023

Èlektron. model.

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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 addi-tional 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 me¬thods demonstrates a substantial improvement in accuracy.

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“…Introduction. Intelligent analysis of biomedical datasets by machine learning tools is a difficult task due to many features of such data, in particular [1,2]:  the multiparametric nature of such datasets;  the need to take into account medical, biological, engineering, and technical features of biomedical datasets;  complex non-linear interconnections inside of the tabular dataset;  the presence of both numerical and categorical features;  the presence of a large number of omissions, anomalies and outliers that occur during data collection;  etc. All this significantly affects the accuracy and generalization properties of machine learning (ML) tools.…”

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confidence: 99%

An Ensemble Method for the Regression Model Parameter Adjustments: Direct Approach

Izonin

2023

mcm-tech

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Intelligence analysis of tabular datasets in the field of biomedical engineering is a complex task. This is explained both by the multidimensional datasets and the complex relationships between the components of the set, and by the high price of the error in the prediction. The task becomes more difficult in the case of limited data for training, which often occurs in this field. This is due to the enormous time, material, or human resources required to collect enough data to implement training procedures with classical machine learning tools. This paper presents a new approach to solving this task. The author has developed a new ensemble method for the regression model parameters adjustments (direct approach) with the possibility of cyclically increasing the accuracy of intellectual analysis of short datasets. The basis of the method is the use of the rational fraction and two machine learning algorithms for its parametric identification. Modeling of the method's efficiency on a real-world short set of data from the field of biomedical engineering demonstrated the high accuracy of the developed method's operation. In particular, the prediction accuracy of the General Regression Neural Network was increased by more than 14% (based on the coefficient of determination. That is why the developed method can be used to solve various applied biomedical engineering tasks in the case of the need to analyze small amounts of data

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Analysis of Deep Learning Methods in Adaptation to the Small Data Problem Solving

Cited by 11 publications

References 26 publications

An Unsupervised-Supervised Ensemble Technology With Non-Iterative Training Algorithm for Small Biomedical Data Analysis

An Unsupervised-Supervised Ensemble Technology With Non-Iterative Training Algorithm for Small Biomedical Data Analysis

An Ensemble Method for the Analysis of Small Biomedical Data based on a Neural Network Without Training

An Ensemble Method for the Regression Model Parameter Adjustments: Direct Approach

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