An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)

Abstract

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