RMDL: Random Multimodel Deep Learning for Classification

The continually increasing number of complex datasets each year necessitates ever improving machine learning methods for robust and accurate categorization of these data. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Text Classification 20NEWS RMDL (15 RDLs) Accuracy 87.91 # 5
Image Classification CIFAR-10 RMDL (30 RDLs) Percentage correct 91.21 # 118
Hierarchical Text Classification of Blurbs (GermEval 2019) LOCAL DATASET RMDL (15 RDLs Accuracy (%) 90.79 # 1
Unsupervised Pre-training Measles RMDL Accuracy (%) 0.1 # 5
Image Classification MNIST RMDL (30 RDLs) Percentage error 0.18 # 5
Accuracy 99.82 # 5
Unsupervised Pre-training UCI measles RMDL 3 RDLs Sensitivity 0.8739 # 2
Unsupervised Pre-training UCI measles Sensitivity 89.1 # 1
Unsupervised Pre-training UCI measles RMDL (30 RDLs) Sensitivity (VEB) 90.69 # 1

Methods used in the Paper


METHOD TYPE
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