Kohonen Network-Based Adaptation of Non Sequential Data for Use in Convolutional Neural Networks
Convolutional neural networks have become one of the most powerful computing tools of artificial intelligence in recent years. They are especially suitable for the analysis of images and other data that have an inherent sequence structure, such as time series data. In the case of data in the form of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/21/7221 |
| _version_ | 1797511782380601344 |
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| author | Michał Bereta |
| author_facet | Michał Bereta |
| author_sort | Michał Bereta |
| collection | DOAJ |
| description | Convolutional neural networks have become one of the most powerful computing tools of artificial intelligence in recent years. They are especially suitable for the analysis of images and other data that have an inherent sequence structure, such as time series data. In the case of data in the form of vectors of features, the order of which does not matter, the use of convolutional neural networks is not justified. This paper presents a new method of representing non-sequential data as images that can be analyzed by a convolutional network. The well-known Kohonen network was used for this purpose. After training on non-sequential data, each example is represented by so-called U-image that can be used as input to a convolutional layer. A hybrid approach was also presented, where the neural network uses two types of input signals, both U-image representation and the original features. The results of the proposed method on traditional machine learning databases as well as on a difficult classification problem originating from the analysis of measurement data from experiments in particle physics are presented. |
| first_indexed | 2024-03-10T05:51:59Z |
| format | Article |
| id | doaj.art-6ee391cf058242b9989f09627f94bd96 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:51:59Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6ee391cf058242b9989f09627f94bd962023-11-22T21:38:23ZengMDPI AGSensors1424-82202021-10-012121722110.3390/s21217221Kohonen Network-Based Adaptation of Non Sequential Data for Use in Convolutional Neural NetworksMichał Bereta0Department of Computer Science, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, PolandConvolutional neural networks have become one of the most powerful computing tools of artificial intelligence in recent years. They are especially suitable for the analysis of images and other data that have an inherent sequence structure, such as time series data. In the case of data in the form of vectors of features, the order of which does not matter, the use of convolutional neural networks is not justified. This paper presents a new method of representing non-sequential data as images that can be analyzed by a convolutional network. The well-known Kohonen network was used for this purpose. After training on non-sequential data, each example is represented by so-called U-image that can be used as input to a convolutional layer. A hybrid approach was also presented, where the neural network uses two types of input signals, both U-image representation and the original features. The results of the proposed method on traditional machine learning databases as well as on a difficult classification problem originating from the analysis of measurement data from experiments in particle physics are presented.https://www.mdpi.com/1424-8220/21/21/7221kohonen networkconvolutional neural networkmultiple input neural networks |
| spellingShingle | Michał Bereta Kohonen Network-Based Adaptation of Non Sequential Data for Use in Convolutional Neural Networks Sensors kohonen network convolutional neural network multiple input neural networks |
| title | Kohonen Network-Based Adaptation of Non Sequential Data for Use in Convolutional Neural Networks |
| title_full | Kohonen Network-Based Adaptation of Non Sequential Data for Use in Convolutional Neural Networks |
| title_fullStr | Kohonen Network-Based Adaptation of Non Sequential Data for Use in Convolutional Neural Networks |
| title_full_unstemmed | Kohonen Network-Based Adaptation of Non Sequential Data for Use in Convolutional Neural Networks |
| title_short | Kohonen Network-Based Adaptation of Non Sequential Data for Use in Convolutional Neural Networks |
| title_sort | kohonen network based adaptation of non sequential data for use in convolutional neural networks |
| topic | kohonen network convolutional neural network multiple input neural networks |
| url | https://www.mdpi.com/1424-8220/21/21/7221 |
| work_keys_str_mv | AT michałbereta kohonennetworkbasedadaptationofnonsequentialdataforuseinconvolutionalneuralnetworks |