  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. Deep learning models
have achieved state-of-the-art results across many domains. RMDL solves the
problem of finding the best deep learning structure and architecture while
simultaneously improving robustness and accuracy through ensembles of deep
learning architectures. RDML can accept as input a variety data to include
text, video, images, and symbolic. This paper describes RMDL and shows test
results for image and text data including MNIST, CIFAR-10, WOS, Reuters, IMDB,
and 20newsgroup. These test results show that RDML produces consistently better
performance than standard methods over a broad range of data types and
classification problems.
