An enhanced gated recurrent unit with auto-encoder for solving text classification problems

Classification has become an important task for categorizing documents automatically based on their respective groups. Gated Recurrent Unit (GRU) is a type of Recurrent Neural Networks (RNNs), and a deep learning algorithm that contains update gate and reset gate. It is considered as one of the m...

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Main Author:	Zulqarnain, Muhammad
Format:	Thesis
Language:	English English English
Published:	2020
Subjects:	QA75 Electronic computers Computer science T Technology (General)
Online Access:	http://eprints.uthm.edu.my/4928/1/24p%20MUHAMMAD%20ZULQARNAIN.pdf http://eprints.uthm.edu.my/4928/2/MUHAMMAD%20ZULQARNAIN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/4928/3/MUHAMMAD%20ZULQARNAIN%20WATERMARK.pdf
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spelling	my-uthm-ep.49282022-02-03T03:10:56Z An enhanced gated recurrent unit with auto-encoder for solving text classification problems 2020-11 Zulqarnain, Muhammad QA75 Electronic computers. Computer science T Technology (General) Classification has become an important task for categorizing documents automatically based on their respective groups. Gated Recurrent Unit (GRU) is a type of Recurrent Neural Networks (RNNs), and a deep learning algorithm that contains update gate and reset gate. It is considered as one of the most efficient text classification techniques, specifically on sequential datasets. However, GRU suffered from three major issues when it is applied for solving the text classification problems. The first drawback is the failure in data dimensionality reduction, which leads to low quality solution for the classification problems. Secondly, GRU still has difficulty in training procedure due to redundancy between update and reset gates. The reset gate creates complexity and require high processing time. Thirdly, GRU also has a problem with informative features loss in each recurrence during the training phase and high computational cost. The reason behind this failure is due to a random selection of features from datasets (or previous outputs), when applied in its standard form. Therefore, in this research, a new model namely Encoder Simplified GRU (ES-GRU) is proposed to reduce dimension of data using an Auto-Encoder (AE). Accordingly, the reset gate is replaced with an update gate in order to reduce the redundancy and complexity in the standard GRU. Finally, a Batch Normalization method is incorporated in the GRU and AE for improving the performance of the proposed ES-GRU model. The proposed model has been evaluated on seven benchmark text datasets and compared with six baselines well-known multiclass text classification approaches included standard GRU, AE, Long Short Term Memory, Convolutional Neural Network, Support Vector Machine, and Naïve Bayes. Based on various types of performance evaluation parameters, a considerable amount of improvement has been observed in the performance of the proposed model as compared to other standard classification techniques, and showed better effectiveness and efficiency of the developed model. 2020-11 Thesis http://eprints.uthm.edu.my/4928/ http://eprints.uthm.edu.my/4928/1/24p%20MUHAMMAD%20ZULQARNAIN.pdf text en public http://eprints.uthm.edu.my/4928/2/MUHAMMAD%20ZULQARNAIN%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/4928/3/MUHAMMAD%20ZULQARNAIN%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA75 Electronic computers Computer science T Technology (General)
spellingShingle	QA75 Electronic computers Computer science T Technology (General) Zulqarnain, Muhammad An enhanced gated recurrent unit with auto-encoder for solving text classification problems
description	Classification has become an important task for categorizing documents automatically based on their respective groups. Gated Recurrent Unit (GRU) is a type of Recurrent Neural Networks (RNNs), and a deep learning algorithm that contains update gate and reset gate. It is considered as one of the most efficient text classification techniques, specifically on sequential datasets. However, GRU suffered from three major issues when it is applied for solving the text classification problems. The first drawback is the failure in data dimensionality reduction, which leads to low quality solution for the classification problems. Secondly, GRU still has difficulty in training procedure due to redundancy between update and reset gates. The reset gate creates complexity and require high processing time. Thirdly, GRU also has a problem with informative features loss in each recurrence during the training phase and high computational cost. The reason behind this failure is due to a random selection of features from datasets (or previous outputs), when applied in its standard form. Therefore, in this research, a new model namely Encoder Simplified GRU (ES-GRU) is proposed to reduce dimension of data using an Auto-Encoder (AE). Accordingly, the reset gate is replaced with an update gate in order to reduce the redundancy and complexity in the standard GRU. Finally, a Batch Normalization method is incorporated in the GRU and AE for improving the performance of the proposed ES-GRU model. The proposed model has been evaluated on seven benchmark text datasets and compared with six baselines well-known multiclass text classification approaches included standard GRU, AE, Long Short Term Memory, Convolutional Neural Network, Support Vector Machine, and Naïve Bayes. Based on various types of performance evaluation parameters, a considerable amount of improvement has been observed in the performance of the proposed model as compared to other standard classification techniques, and showed better effectiveness and efficiency of the developed model.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Zulqarnain, Muhammad
author_facet	Zulqarnain, Muhammad
author_sort	Zulqarnain, Muhammad
title	An enhanced gated recurrent unit with auto-encoder for solving text classification problems
title_short	An enhanced gated recurrent unit with auto-encoder for solving text classification problems
title_full	An enhanced gated recurrent unit with auto-encoder for solving text classification problems
title_fullStr	An enhanced gated recurrent unit with auto-encoder for solving text classification problems
title_full_unstemmed	An enhanced gated recurrent unit with auto-encoder for solving text classification problems
title_sort	enhanced gated recurrent unit with auto-encoder for solving text classification problems
granting_institution	Universiti Tun Hussein Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2020
url	http://eprints.uthm.edu.my/4928/1/24p%20MUHAMMAD%20ZULQARNAIN.pdf http://eprints.uthm.edu.my/4928/2/MUHAMMAD%20ZULQARNAIN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/4928/3/MUHAMMAD%20ZULQARNAIN%20WATERMARK.pdf
_version_	1747831058812895232

An enhanced gated recurrent unit with auto-encoder for solving text classification problems

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