Functional data learning using convolutional neural networks
In this paper, we show how convolutional neural networks (CNNs) can be used in regression and classification learning problems for noisy and non-noisy functional data (FD). The main idea is to transform the FD into a 28 by 28 image. We use a specific but typical architecture of a CNN to perform all...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/ad2627 |
| _version_ | 1797303038879203328 |
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| author | J Galarza T Oraby |
| author_facet | J Galarza T Oraby |
| author_sort | J Galarza |
| collection | DOAJ |
| description | In this paper, we show how convolutional neural networks (CNNs) can be used in regression and classification learning problems for noisy and non-noisy functional data (FD). The main idea is to transform the FD into a 28 by 28 image. We use a specific but typical architecture of a CNN to perform all the regression exercises of parameter estimation and functional form classification. First, we use some functional case studies of FD with and without random noise to showcase the strength of the new method. In particular, we use it to estimate exponential growth and decay rates, the bandwidths of sine and cosine functions, and the magnitudes and widths of curve peaks. We also use it to classify the monotonicity and curvatures of FD, the algebraic versus exponential growth, and the number of peaks of FD. Second, we apply the same CNNs to Lyapunov exponent estimation in noisy and non-noisy chaotic data, in estimating rates of disease transmission from epidemic curves, and in detecting the similarity of drug dissolution profiles. Finally, we apply the method to real-life data to detect Parkinson’s disease patients in a classification problem. We performed ablation analysis and compared the new method with other commonly used neural networks for FD and showed that it outperforms them in all applications. Although simple, the method shows high accuracy and is promising for future use in engineering and medical applications. |
| first_indexed | 2024-03-07T23:47:12Z |
| format | Article |
| id | doaj.art-76d3a5b0d0824d3f80802456a37f6fcf |
| institution | Directory Open Access Journal |
| issn | 2632-2153 |
| language | English |
| last_indexed | 2024-03-07T23:47:12Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj.art-76d3a5b0d0824d3f80802456a37f6fcf2024-02-19T11:07:24ZengIOP PublishingMachine Learning: Science and Technology2632-21532024-01-015101503010.1088/2632-2153/ad2627Functional data learning using convolutional neural networksJ Galarza0https://orcid.org/0000-0002-1994-5931T Oraby1School of Mathematical and Statistical Sciences, The University of Texas Rio Grande Valley , Edinburg, TX 78539, United States of AmericaSchool of Mathematical and Statistical Sciences, The University of Texas Rio Grande Valley , Edinburg, TX 78539, United States of AmericaIn this paper, we show how convolutional neural networks (CNNs) can be used in regression and classification learning problems for noisy and non-noisy functional data (FD). The main idea is to transform the FD into a 28 by 28 image. We use a specific but typical architecture of a CNN to perform all the regression exercises of parameter estimation and functional form classification. First, we use some functional case studies of FD with and without random noise to showcase the strength of the new method. In particular, we use it to estimate exponential growth and decay rates, the bandwidths of sine and cosine functions, and the magnitudes and widths of curve peaks. We also use it to classify the monotonicity and curvatures of FD, the algebraic versus exponential growth, and the number of peaks of FD. Second, we apply the same CNNs to Lyapunov exponent estimation in noisy and non-noisy chaotic data, in estimating rates of disease transmission from epidemic curves, and in detecting the similarity of drug dissolution profiles. Finally, we apply the method to real-life data to detect Parkinson’s disease patients in a classification problem. We performed ablation analysis and compared the new method with other commonly used neural networks for FD and showed that it outperforms them in all applications. Although simple, the method shows high accuracy and is promising for future use in engineering and medical applications.https://doi.org/10.1088/2632-2153/ad2627functional data learningdeep learningconvolutional neural networksregressionclassification |
| spellingShingle | J Galarza T Oraby Functional data learning using convolutional neural networks Machine Learning: Science and Technology functional data learning deep learning convolutional neural networks regression classification |
| title | Functional data learning using convolutional neural networks |
| title_full | Functional data learning using convolutional neural networks |
| title_fullStr | Functional data learning using convolutional neural networks |
| title_full_unstemmed | Functional data learning using convolutional neural networks |
| title_short | Functional data learning using convolutional neural networks |
| title_sort | functional data learning using convolutional neural networks |
| topic | functional data learning deep learning convolutional neural networks regression classification |
| url | https://doi.org/10.1088/2632-2153/ad2627 |
| work_keys_str_mv | AT jgalarza functionaldatalearningusingconvolutionalneuralnetworks AT toraby functionaldatalearningusingconvolutionalneuralnetworks |