Entropy-extreme concept of data gaps filling in a small-sized collection
Annotation: The article investigates the process of filling data gaps in a small-sized collection, which generalizes information about periodic measurement of input and output parameters of a target object. To fill the data gaps, a concept is proposed based on generating a committee of entropy-optim...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Egyptian Informatics Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866525000143 |
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| author | Viacheslav Kovtun Krzysztof Grochla Mohammed Al-Maitah Saad Aldosary Oleksii Kozachko |
| author_facet | Viacheslav Kovtun Krzysztof Grochla Mohammed Al-Maitah Saad Aldosary Oleksii Kozachko |
| author_sort | Viacheslav Kovtun |
| collection | DOAJ |
| description | Annotation: The article investigates the process of filling data gaps in a small-sized collection, which generalizes information about periodic measurement of input and output parameters of a target object. To fill the data gaps, a concept is proposed based on generating a committee of entropy-optimal trajectories through sampling probability density functions of parameters from a stochastic parameterized model trained on relevant data. The concept is generalized to cases of filling gaps in output data, input data, and both those data spaces. Filling gaps in output data is implemented using entropy-extreme estimation of probability density functions for parameters of the model and errors of measurement. In the case of addressing missing values in input data, these are interpreted as results of transforming a sequence of independent stochastic vectors introduced into a model structurally identical to that formalized for filling gaps in output data. Thus, the proposed concept inherits the benefits of both parametric estimation and using a trained model of the target process and non-parametric estimation of undefined characteristics that distort data. The proposed concept was tested on the task of filling gaps in a collection consisting of 35 tuples with measurement results of three attributes. It was considered that the imperfection of the measurement procedure caused variability in the obtained data at the level of 15% of their absolute value. Less than 20% of the data from the collection was used to train the corresponding entropy-extreme model. The relative error of the filled missing data was 0.21. |
| format | Article |
| id | doaj-art-daa23902a70e4b879c15caba598babc7 |
| institution | Kabale University |
| issn | 1110-8665 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-daa23902a70e4b879c15caba598babc72025-02-12T05:30:44ZengElsevierEgyptian Informatics Journal1110-86652025-03-0129100621Entropy-extreme concept of data gaps filling in a small-sized collectionViacheslav Kovtun0Krzysztof Grochla1Mohammed Al-Maitah2Saad Aldosary3Oleksii Kozachko4Computer Control Systems Department, Faculty of Intelligent Information Technologies and Automation, Vinnytsia National Technical University, Khmelnitske Shose str., 95, Vinnytsia 21000, Ukraine; Corresponding author.Internet of Things Group, Institute of Theoretical and Applied Informatics Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, PolandComputer Science Department, Community College, King Saud University, 11451 Riyadh, Saudi ArabiaComputer Science Department, Community College, King Saud University, 11451 Riyadh, Saudi ArabiaSystem Analysis and Information Technologies Department, Faculty of Intelligent Information Technologies and Automation, Vinnytsia National Technical University, Khmelnitske Shose str., 95, Vinnytsia 21000, UkraineAnnotation: The article investigates the process of filling data gaps in a small-sized collection, which generalizes information about periodic measurement of input and output parameters of a target object. To fill the data gaps, a concept is proposed based on generating a committee of entropy-optimal trajectories through sampling probability density functions of parameters from a stochastic parameterized model trained on relevant data. The concept is generalized to cases of filling gaps in output data, input data, and both those data spaces. Filling gaps in output data is implemented using entropy-extreme estimation of probability density functions for parameters of the model and errors of measurement. In the case of addressing missing values in input data, these are interpreted as results of transforming a sequence of independent stochastic vectors introduced into a model structurally identical to that formalized for filling gaps in output data. Thus, the proposed concept inherits the benefits of both parametric estimation and using a trained model of the target process and non-parametric estimation of undefined characteristics that distort data. The proposed concept was tested on the task of filling gaps in a collection consisting of 35 tuples with measurement results of three attributes. It was considered that the imperfection of the measurement procedure caused variability in the obtained data at the level of 15% of their absolute value. Less than 20% of the data from the collection was used to train the corresponding entropy-extreme model. The relative error of the filled missing data was 0.21.http://www.sciencedirect.com/science/article/pii/S1110866525000143Machine learningData analysisData gap fillingSmall-sized collectionEntropy-extreme estimationErrors in measurement |
| spellingShingle | Viacheslav Kovtun Krzysztof Grochla Mohammed Al-Maitah Saad Aldosary Oleksii Kozachko Entropy-extreme concept of data gaps filling in a small-sized collection Egyptian Informatics Journal Machine learning Data analysis Data gap filling Small-sized collection Entropy-extreme estimation Errors in measurement |
| title | Entropy-extreme concept of data gaps filling in a small-sized collection |
| title_full | Entropy-extreme concept of data gaps filling in a small-sized collection |
| title_fullStr | Entropy-extreme concept of data gaps filling in a small-sized collection |
| title_full_unstemmed | Entropy-extreme concept of data gaps filling in a small-sized collection |
| title_short | Entropy-extreme concept of data gaps filling in a small-sized collection |
| title_sort | entropy extreme concept of data gaps filling in a small sized collection |
| topic | Machine learning Data analysis Data gap filling Small-sized collection Entropy-extreme estimation Errors in measurement |
| url | http://www.sciencedirect.com/science/article/pii/S1110866525000143 |
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