An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL) Article Swipe
Mojtaba Heidarysafa
,
Kamran Kowsari
,
Donald E. Brown
,
Kiana Jafari Meimandi
,
Laura E. Barnes
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.18178/ijmlc.2018.8.4.703
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.18178/ijmlc.2018.8.4.703
The exponential growth in the number of complex datasets every year requires\nmore enhancement in machine learning methods to provide robust and accurate\ndata classification. Lately, deep learning approaches have achieved surpassing\nresults in comparison to previous machine learning algorithms. However, finding\nthe suitable structure for these models has been a challenge for researchers.\nThis paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble,\ndeep learning approach for classification. RMDL solves the problem of finding\nthe best deep learning structure and architecture while simultaneously\nimproving robustness and accuracy through ensembles of deep learning\narchitectures. In short, RMDL trains multiple randomly generated models of Deep\nNeural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural\nNetwork (RNN) in parallel and combines their results to produce better result\nof any of those models individually. In this paper, we describe RMDL model and\ncompare the results for image and text classification as well as face\nrecognition. We used MNIST and CIFAR-10 datasets as ground truth datasets for\nimage classification and WOS, Reuters, IMDB, and 20newsgroup datasets for text\nclassification. Lastly, we used ORL dataset to compare the model performance on\nface recognition task.\n
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18178/ijmlc.2018.8.4.703
- http://www.ijml.org/vol8/703-SDM18-226.pdf
- OA Status
- diamond
- Cited By
- 34
- References
- 89
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2888503998
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- OpenAlex ID
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https://openalex.org/W2888503998Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18178/ijmlc.2018.8.4.703Digital Object Identifier
- Title
-
An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-08-01Full publication date if available
- Authors
-
Mojtaba Heidarysafa, Kamran Kowsari, Donald E. Brown, Kiana Jafari Meimandi, Laura E. BarnesList of authors in order
- Landing page
-
https://doi.org/10.18178/ijmlc.2018.8.4.703Publisher landing page
- PDF URL
-
https://www.ijml.org/vol8/703-SDM18-226.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://www.ijml.org/vol8/703-SDM18-226.pdfDirect OA link when available
- Concepts
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Artificial intelligence, MNIST database, Computer science, Deep learning, Convolutional neural network, Machine learning, Robustness (evolution), Ensemble learning, Pattern recognition (psychology), Artificial neural network, Contextual image classification, Image (mathematics), Gene, Chemistry, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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34Total citation count in OpenAlex
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2024: 5, 2023: 3, 2022: 5, 2021: 5, 2020: 9Per-year citation counts (last 5 years)
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89Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.a | 46, 57 |
| abstract_inverted_index.In | 86, 121 |
| abstract_inverted_index.We | 140 |
| abstract_inverted_index.as | 136, 138, 146 |
| abstract_inverted_index.in | 3, 13, 30, 106 |
| abstract_inverted_index.of | 6, 68, 83, 94, 117 |
| abstract_inverted_index.to | 17, 32, 112, 166 |
| abstract_inverted_index.we | 124, 162 |
| abstract_inverted_index.ORL | 164 |
| abstract_inverted_index.The | 0 |
| abstract_inverted_index.and | 20, 74, 79, 102, 108, 133, 143, 152, 156 |
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| abstract_inverted_index.new | 58 |
| abstract_inverted_index.the | 4, 66, 129, 168 |
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| abstract_inverted_index.(RNN) | 105 |
| abstract_inverted_index.IMDB, | 155 |
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| abstract_inverted_index.image | 132 |
| abstract_inverted_index.model | 127, 169 |
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| abstract_inverted_index.truth | 148 |
| abstract_inverted_index.while | 76 |
| abstract_inverted_index.(DNN), | 97 |
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| abstract_inverted_index.ground | 147 |
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| abstract_inverted_index.number | 5 |
| abstract_inverted_index.paper, | 123 |
| abstract_inverted_index.robust | 19 |
| abstract_inverted_index.short, | 87 |
| abstract_inverted_index.solves | 65 |
| abstract_inverted_index.trains | 89 |
| abstract_inverted_index.(RMDL): | 56 |
| abstract_inverted_index.Lastly, | 161 |
| abstract_inverted_index.Lately, | 23 |
| abstract_inverted_index.Network | 96, 100 |
| abstract_inverted_index.compare | 167 |
| abstract_inverted_index.complex | 7 |
| abstract_inverted_index.dataset | 165 |
| abstract_inverted_index.machine | 14, 34 |
| abstract_inverted_index.methods | 16 |
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| abstract_inverted_index.results | 111, 130 |
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| abstract_inverted_index.CIFAR-10 | 144 |
| abstract_inverted_index.However, | 37 |
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| abstract_inverted_index.accuracy | 80 |
| abstract_inverted_index.achieved | 28 |
| abstract_inverted_index.approach | 61 |
| abstract_inverted_index.combines | 109 |
| abstract_inverted_index.datasets | 8, 145, 149, 158 |
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| abstract_inverted_index.multiple | 90 |
| abstract_inverted_index.on\nface | 171 |
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| abstract_inverted_index.randomly | 91 |
| abstract_inverted_index.suitable | 39 |
| abstract_inverted_index.Recurrent | 103 |
| abstract_inverted_index.challenge | 47 |
| abstract_inverted_index.ensembles | 82 |
| abstract_inverted_index.generated | 92 |
| abstract_inverted_index.structure | 40, 73 |
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