A Robust Text Classifier Based on Denoising Deep Neural Network in the Analysis of Big Data

Wulamu, Aziguli; Zhang, Yuanyu; Xie, Ying; Zhang, Dezheng; Luo, Xiong; Li, Chunmiao; Zhang, Yao

doi:10.1155/2017/3610378

Cited by 18 publications

(14 citation statements)

References 25 publications

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“…The DNN with SVR at the top layer was found to be better than the conventional SVR in forecasting hourly temperatures. [82] propose text classifier based on denoising deep neural network (DDNN) to improve the accuracy of big text data classification. The dataset collected for the study were 20-Newsgroup and BBC news documents.…”

Section: Deep Neural Networkmentioning

confidence: 99%

Progress on Artificial Neural Networks for Big Data Analytics: A Survey

et al. 2019

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Approximately 2.5 quintillion bytes of data are emitted on a daily basis, and this has brought the world into the era of ''big data.'' Artificial neural networks (ANNs) are known for their effectiveness and efficiency for small datasets, and this era of big data has posed a challenge to the big data analytics using ANN. Recently, much research effort has been devoted to the application of the ANN in big data analytics and is still ongoing, although it is in it is early stages. The purpose of this paper is to summarize recent progress, challenges, and opportunities for future research. This paper presents a concise view of the state of the art, challenges, and future research opportunities regarding the applications of the ANN in big data analytics and reveals that progress has been made in this area. Our review points out the limitations of the previous approaches, the challenges in the ANN approaches in terms of their applications in big data analytics, and several ANN architecture that have not yet been explored in big data analytics and opportunities for future research. We believe that this paper can serve as a yardstick for future progress on the applications of the ANN in big data analytics as well as a starting point for new researchers with an interest in the exploration of the ANN in big data analytics. INDEX TERMS Big data analytics, artificial neural networks, evolutionary neural network, convolutional neural network, dataset. The associate editor coordinating the review of this manuscript and approving it for publication was Shirui Pan. many organizations on an ongoing basis. These datasets are being collected from various sources, including but not limited to the World Wide Web (WWW), social networks and sensor networks [3]. The discovery of knowledge from unstructured data accumulated from the WWW remains a difficult task because the content is suitable for human consumption rather than for machines [4]. Experimental evidence has shown that if big datasets are exploited and managed properly, it can give rise to critical intelligence that can motivate informed decisions and

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Section: Deep Neural Networkmentioning

confidence: 99%

Progress on Artificial Neural Networks for Big Data Analytics: A Survey

et al. 2019

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“…It produced the performance of 96.49% on four classes (politics,comp,religion,rec) of 20 newsgroup dataset. Likewise, Aziguli et al [2] proposed denoising deep neural networks exploited restricted boltzmann machine and denoising autoencoder to produce the performance of 75%, and 97% on 20 newsgroup, and BBC datasets respectively. Whereas, deep belief network and softmax regression were combinely used [36] to select discriminative features for text classification.…”

Section: A Comparison With State-of-the-artmentioning

confidence: 99%

“…Broadly, text classification methodologies are divided into two classes statistical, and rule-based [2]. Statistical approaches utilize arithmetical knowledge, whereas rule-based approaches require extensive domain knowledge to develop rules on the basis of which samples could be classified into a predefined set of categories.…”

Section: Introductionmentioning

confidence: 99%

A Robust Hybrid Approach for Textual Document Classification

Asim

Khan

Malik

et al. 2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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Text document classification is an important task for diverse natural language processing based applications. Traditional machine learning approaches mainly focused on reducing dimensionality of textual data to perform classification. This although improved the overall classification accuracy, the classifiers still faced sparsity problem due to lack of better data representation techniques. Deep learning based text document classification, on the other hand, benefitted greatly from the invention of word embeddings that have solved the sparsity problem and researchers focus mainly remained on the development of deep architectures. Deeper architectures, however, learn some redundant features that limit the performance of deep learning based solutions. In this paper, we propose a two stage text document classification methodology which combines traditional feature engineering with automatic feature engineering (using deep learning). The proposed methodology comprises a filter based feature selection (FSE) algorithm followed by a deep convolutional neural network. This methodology is evaluated on the two most commonly used public datasets, i.e., 20 Newsgroups data and BBC news data. Evaluation results reveal that the proposed methodology outperforms the state-of-the-art of both the (traditional) machine learning and deep learning based text document classification methodologies with a significant margin of 7.7% on 20 Newsgroups and 6.6% on BBC news datasets.

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“…In the case of foundations of scientific programming, the work in [173] reviewing the main foundations of scientific programming techniques and the use of pattern matching techniques in large graphs [174] are examples of improvements and works in the scope of software techniques. Finally, applications in coal mining [175], recommendation engines for car sharing services [176], health risk prediction [177], text classification [178], or information security [179] are domains in which data are continuously being generated representing good candidates to apply scientific programming techniques.…”

Section: Data-intensive Engineering Environments and Scientificmentioning

confidence: 99%

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Álvarez-Rodríguez

Alor-Hernández

Mejia-Miranda

2018

Scientific Programming

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The present paper introduces and reviews existing technology and research works in the field of scientific programming methods and techniques in data-intensive engineering environments. More specifically, this survey aims to collect those relevant approaches that have faced the challenge of delivering more advanced and intelligent methods taking advantage of the existing large datasets. Although existing tools and techniques have demonstrated their ability to manage complex engineering processes for the development and operation of safety-critical systems, there is an emerging need to know how existing computational science methods will behave to manage large amounts of data. That is why, authors review both existing open issues in the context of engineering with special focus on scientific programming techniques and hybrid approaches. 1193 journal papers have been found as the representative in these areas screening 935 to finally make a full review of 122. Afterwards, a comprehensive mapping between techniques and engineering and nonengineering domains has been conducted to classify and perform a meta-analysis of the current state of the art. As the main result of this work, a set of 10 challenges for future data-intensive engineering environments have been outlined.

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A Robust Text Classifier Based on Denoising Deep Neural Network in the Analysis of Big Data

Cited by 18 publications

References 25 publications

Progress on Artificial Neural Networks for Big Data Analytics: A Survey

Progress on Artificial Neural Networks for Big Data Analytics: A Survey

A Robust Hybrid Approach for Textual Document Classification

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

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