Abstract—The exponential growth in the number of complex datasets every year requires more enhancement in machine learning methods to provide robust and accurate data classification. Lately, deep learning approaches have achieved surpassing results in comparison to previous machine learning algorithms. However, finding the suitable structure for these models has been a challenge for researchers. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. 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. In short, RMDL trains multiple randomly generated models of Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in parallel and combines their results to produce better result of any of those models individually. In this paper, we describe RMDL model and compare the results for image and text classification as well as face recognition. We used MNIST and CIFAR-10 datasets as ground truth datasets for image classification and WOS, Reuters, IMDB, and 20newsgroup datasets for text classification. Lastly, we used ORL dataset to compare the model performance on face recognition task.
Index Terms—Deep neural networks, document classification, hierarchical learning, multimodel deep learning.
The authors are with the Department of System and Information Engineering, University of Virginia, Charlottesville, VA 22911 USA (e-mail: {kk7nc, mh4pk, deb, kj6vd, lb3dp}@virginia.edu).
Cite: Mojtaba Heidarysafa, Kamran Kowsari, Donald E. Brown, Kiana Jafari Meimandi, and Laura E. Barnes, "An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)," International Journal of Machine Learning and Computing vol. 8, no. 4, pp. 298-310, 2018.