Home
All codes and Datasets: coming soon
Refrenced paper : RMDL: Random Multimodel Deep Learning for Classification
Refrenced paper : An Improvement of Data Classification using 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 asinput a variety data to include text, video, images, and symbolic.
Overview of RDML: Random Multimodel Deep Learning for classification. The RMDL includesnRandom modelswhich aredrandom model of DNN classifiers,cmodels of CNN classifiers, andrRNN classifiers wherer+c+d=n.
Random Multimodel Deep Learning (RDML) architecture for classification. RMDL includes 3 Random models, oneDNN classifier at left, one Deep CNN classifier at middle, and one Deep RNN classifier at right (each unit could be LSTMor GRU).
There are git RMDL in this repository; to clone all the needed files, please use:
git clone --recursive https://github.com/kk7nc/RMDL.git
he primary requirements for this package are Python 3 with Tensorflow version 1.0.1 or greater. The requirements.txt file contains a listing of the required Python packages; to install all requirements, run the following:
pip -r install requirements.txt
Or
pip3 install -r requirements.txt
Or:
conda install --file requirements.txt
If the above command does not work, use the following:
sudo -H pip install -r requirements.txt
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 been achieved surpassing results in comparison to previous machine learning algorithms on tasks such as image classification, natural language processing, face recognition, and etc. The success of these deep learning algorithms relys on their capacity to model complex and non-linear relationships between data. 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 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. To create these models, each deep learning model has been constructed in a random fashion regarding the number of layers and nodes in their neural network structure. The resulting RDML model can be used for various domains such as text, video, images, and symbolic. In this paper, we describe RMDL model in depth and show the results for image and text classification as well as face recognition. For image classification, we compared our model with some of the available baselines using MNIST and CIFAR-10 datasets. Similarly, we used four datasets namely, WOS, Reuters, IMDB, and 20newsgroup and compared our results with available baselines. Web of Science (WOS) has been collected by authors and consists of three sets (small, medium and large set). Lastly, we used ORL dataset to compare the performance with other face recognition methods. These test results show that RDML model consistently outperform standard methods over a broad range of data types and classification problems.
-
- This dataset contains 50,000 documents with 2 categories.
-
- This dataset contains 21,578 documents with 90 categories.
-
- This dataset contains 20,000 documents with 20 categories.
-
Web of Science Dataset (DOI: 10.17632/9rw3vkcfy4.2)
-
Web of Science Dataset WOS-11967
- This dataset contains 11,967 documents with 35 categories which include 7 parents categories.
-
Web of Science Dataset WOS-46985
- This dataset contains 46,985 documents with 134 categories which include 7 parents categories.
-
Web of Science Dataset WOS-5736
- This dataset contains 5,736 documents with 11 categories which include 3 parents categories.
-
-
- The MNIST database contains 60,000 training images and 10,000 testing images.
-
- The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
General:
-
Python 3.5 or later see Instruction Documents
-
TensorFlow see Instruction Documents.
-
scikit-learn see Instruction Documents
-
Keras see Instruction Documents
-
scipy see Instruction Documents
-
GPU (if you want to run on GPU):
-
CUDA® Toolkit 8.0. For details, see NVIDIA's documentation.
-
cuDNN v6. For details, see NVIDIA's documentation.
-
GPU card with CUDA Compute Capability 3.0 or higher.
-
The libcupti-dev library,
-
To install this library, issue the following command:
$ sudo apt-get install libcupti-dev
-
Download GloVe: Global Vectors for Word Representation Instruction Documents
- Set data directory into Global.py
Send an email to [email protected]
@inproceedings{Kowsari2018RMDL,
title={RMDL: Random Multimodel Deep Learning for Classification},
author={Kowsari, Kamran and Heidarysafa, Mojtaba and Brown, Donald E. and Jafari Meimandi, Kiana and Barnes, Laura E.},
booktitle={Proceedings of the 2018 International Conference on Information System and Data Mining},
year={2018},
organization={ACM}
}
And
@inproceedings{Heidarysafa2018RMDL,
title={An Improvement of Data Classification using Random Multimodel Deep Learning (RMDL)},
author={Heidarysafa, Mojtaba and Kowsari, Kamran and Brown, Donald E. and Jafari Meimandi, Kiana and Barnes, Laura E.},
booktitle={International Journal of Machine Learning and Computing (IJMLC)},
year={2018}
}