RMDL

Kowsari, Kamran; Heidarysafa, Mojtaba; Brown, Donald E.; Meimandi, Kiana Jafari; Barnes, Laura E.

doi:10.1145/3206098.3206111

Cited by 66 publications

(5 citation statements)

References 48 publications

(51 reference statements)

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“…Once the training set has been gathered, it is the moment to build the model. Random Multimodel Deep Learning (RMDL) has been used [42] and [43], as it shows accuracies over 90% in text classification tasks. It stands out for using Deep Neural Network (DNN), CNN and RNN which have demonstrated a good performance classifying texts [38].…”

Section: Methodsmentioning

confidence: 99%

A domain categorisation of vocabularies based on a deep learning classifier

Nogales

Sicilia

García‐Tejedor

2021

Journal of Information Science

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The publication of large amounts of open data is an increasing trend. This is a consequence of initiatives like Linked Open Data (LOD) that aims at publishing and linking data sets published in the World Wide Web. Linked Data publishers should follow a set of principles for their task. This information is described in a 2011 document that includes the consideration of reusing vocabularies as key. The Linked Open Vocabularies (LOV) project attempts to collect the vocabularies and ontologies commonly used in LOD. These ontologies have been classified by domain following the criteria of LOV members, thus having the disadvantage of introducing personal biases. This article presents an automatic classifier of ontologies based on the main categories appearing in Wikipedia. For that purpose, word-embedding models are used in combination with deep learning techniques. Results show that with a hybrid model of regular Deep Neural Networks (DNNs), Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), classification could be made with an accuracy of 93.57%. A further evaluation of the domain matchings between LOV and the classifier brings possible matchings in 79.8% of the cases.

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Section: Methodsmentioning

confidence: 99%

A domain categorisation of vocabularies based on a deep learning classifier

Nogales

Sicilia

García‐Tejedor

2021

Journal of Information Science

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“…Random multimodel deep learning (RMDL) was introduced by K. Kowsari et al [4,5] as a novel deep learning technique for classification. RMDL can be used in any kind of data set for classification.…”

Section: Random Multimodel Deep Learning (Rmdl)mentioning

confidence: 99%

“…Text classification problems have been widely studied and addressed in many real applications [1][2][3][4][5][6][7][8] over the last few decades. Especially with recent breakthroughs in Natural Language Processing (NLP) and text mining, many researchers are now interested in developing applications that leverage text classification methods.…”

Section: Introductionmentioning

confidence: 99%

Text Classification Algorithms: A Survey

et al. 2019

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In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in real-world problems are discussed.Spelling correction is an optional pre-processing step. Typos (short for typographical errors) are commonly present in texts and documents, especially in social media text data sets (e.g., Twitter). Many algorithms, techniques, and methods have addressed this problem in NLP [49]. Many techniques and methods are available for researchers including hashing-based and context-sensitive spelling correction techniques [50], as well as spelling correction using Trie and Damerau-Levenshtein distance bigram [51]. StemmingIn NLP, one word could appear in different forms (i.e., singular and plural noun form) while the semantic meaning of each form is the same [52]. One method for consolidating different forms of a word into the same feature space is stemming. Text stemming modifies words to obtain variant word forms using different linguistic processes such as affixation (addition of affixes) [53,54]. For example, the stem of the word "studying" is "study". LemmatizationLemmatization is a NLP process that replaces the suffix of a word with a different one or removes the suffix of a word completely to get the basic word form (lemma) [54][55][56]. Syntactic Word RepresentationMany researchers have worked on this text feature extraction technique to solve the loosing syntactic and semantic relation between words. Many researchers addressed novel techniques for solving this problem, but many of these techniques still have limitations. In [57], a model was introduced in which the usefulness of including syntactic and semantic knowledge in the text representation for the selection of sentences comes from technical genomic texts. The other solution for syntactic problem is using the n-gram technique for feature extraction. N-GramThe n-gram technique is a set of n-word which occurs "in that order" in a text set. This is not a representation of a text, but it could be used as a feature to represent a text.BOW is a representation of a text using its words (1-gram) which loses their order (syntactic). This model is very easy to obtain and the text can be represented through a vector, generally of a manageable size of the text. On the ...

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“…For example, [ 31 ] used a simple averaging of the probability outputs from individual models for ASD classification. The authors in [ 34 ] implemented a majority voting algorithm for combining different deep learning classifiers on text, images, and video. We propose an end-to-end deep neural network-based ensemble model that uses the weighting mechanism for assigning different levels of importance for each model’s prediction.…”

Section: Introductionmentioning

confidence: 99%

Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder

Rakhimberdina

Liu

Murata

2020

Sensors

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With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can differentiate between healthy subjects and patients is of great importance. Recently, graph neural networks have found increasing application in domains where the population’s structure is modeled as a graph. The application of graphs for analyzing brain imaging datasets helps to discover clusters of individuals with a specific diagnosis. However, the choice of the appropriate population graph becomes a challenge in practice, as no systematic way exists for defining it. To solve this problem, we propose a population graph-based multi-model ensemble, which improves the prediction, regardless of the choice of the underlying graph. First, we construct a set of population graphs using different combinations of imaging and phenotypic features and evaluate them using Graph Signal Processing tools. Subsequently, we utilize a neural network architecture to combine multiple graph-based models. The results demonstrate that the proposed model outperforms the state-of-the-art methods on Autism Brain Imaging Data Exchange (ABIDE) dataset.

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RMDL

Cited by 66 publications

References 48 publications

A domain categorisation of vocabularies based on a deep learning classifier

A domain categorisation of vocabularies based on a deep learning classifier

Text Classification Algorithms: A Survey

Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder

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