An improved associative classification model using fuzzy parameterized soft set-based decision for text classification

Text classification is applicable in various problem domains, including marketing, security, and biomedical. One of the potential text classifiers is the well-known associative classification approach. However, the existing associative classification approach is still prone to some limitations es...

Full description

Saved in:

Bibliographic Details
Main Author:	Rohidin, Dede
Format:	Thesis
Language:	English English English
Published:	2023
Subjects:	T Technology (General)
Online Access:	http://eprints.uthm.edu.my/10825/1/24p%20DEDE%20ROHIDIN.pdf http://eprints.uthm.edu.my/10825/2/DEDE%20ROHIDIN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/10825/3/DEDE%20ROHIDIN%20WATERMARK.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uthm-ep.10825
record_format	uketd_dc
spelling	my-uthm-ep.108252024-05-13T07:05:26Z An improved associative classification model using fuzzy parameterized soft set-based decision for text classification 2023-03 Rohidin, Dede T Technology (General) Text classification is applicable in various problem domains, including marketing, security, and biomedical. One of the potential text classifiers is the well-known associative classification approach. However, the existing associative classification approach is still prone to some limitations especially when dealing with the problem with too many rules in text classification problem. Some of the rules generated from the textual data may be irrelevant and redundant, result in low performance in imbalanced and class overlapping data. Therefore, this research has proposed an improved associative classification approach to enhance the performance and efficiency of the text classification by removing the irrelevant rules, reducing redundant rules, and handling the imbalanced and class overlapping issues in the textual data. The proposed associative classification approach consists of three stages: pre-processing, fuzzification and classification. In the classification stage primarily, this study proposed to integrating principles of fuzzy soft set theory into associative rules, therefore referred to as Class-Based Fuzzy Soft Associative (CBFSA) method. The experiments used 20 Newsgroup (balanced data) datasets and Reuter-25178 (imbalanced) to evaluate the proposed model. It shows that CBFSA is successful in removing irrelevant and reducing redundant rules. The CBFSA classifier applies smaller number of rules than Class Based Associative (CBA) and Class Based of Predictive Association Rule (CPAR). The CBFSA is also successful in dealing with imbalanced and class overlap data. The CBFSA performance is higher and faster than CBA and CPAR. Meanwhile, comparative analysis with some other non-associative based classifiers may achieve improved f1-measure between 6% to 32%. The processing time of CBFSA is faster than RNN and CNN but slightly slower than Decision Tree, k-NN, Naïve Bayes, Roccio, Bagging and Boosting 2023-03 Thesis http://eprints.uthm.edu.my/10825/ http://eprints.uthm.edu.my/10825/1/24p%20DEDE%20ROHIDIN.pdf text en public http://eprints.uthm.edu.my/10825/2/DEDE%20ROHIDIN%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/10825/3/DEDE%20ROHIDIN%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	T Technology (General)
spellingShingle	T Technology (General) Rohidin, Dede An improved associative classification model using fuzzy parameterized soft set-based decision for text classification
description	Text classification is applicable in various problem domains, including marketing, security, and biomedical. One of the potential text classifiers is the well-known associative classification approach. However, the existing associative classification approach is still prone to some limitations especially when dealing with the problem with too many rules in text classification problem. Some of the rules generated from the textual data may be irrelevant and redundant, result in low performance in imbalanced and class overlapping data. Therefore, this research has proposed an improved associative classification approach to enhance the performance and efficiency of the text classification by removing the irrelevant rules, reducing redundant rules, and handling the imbalanced and class overlapping issues in the textual data. The proposed associative classification approach consists of three stages: pre-processing, fuzzification and classification. In the classification stage primarily, this study proposed to integrating principles of fuzzy soft set theory into associative rules, therefore referred to as Class-Based Fuzzy Soft Associative (CBFSA) method. The experiments used 20 Newsgroup (balanced data) datasets and Reuter-25178 (imbalanced) to evaluate the proposed model. It shows that CBFSA is successful in removing irrelevant and reducing redundant rules. The CBFSA classifier applies smaller number of rules than Class Based Associative (CBA) and Class Based of Predictive Association Rule (CPAR). The CBFSA is also successful in dealing with imbalanced and class overlap data. The CBFSA performance is higher and faster than CBA and CPAR. Meanwhile, comparative analysis with some other non-associative based classifiers may achieve improved f1-measure between 6% to 32%. The processing time of CBFSA is faster than RNN and CNN but slightly slower than Decision Tree, k-NN, Naïve Bayes, Roccio, Bagging and Boosting
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Rohidin, Dede
author_facet	Rohidin, Dede
author_sort	Rohidin, Dede
title	An improved associative classification model using fuzzy parameterized soft set-based decision for text classification
title_short	An improved associative classification model using fuzzy parameterized soft set-based decision for text classification
title_full	An improved associative classification model using fuzzy parameterized soft set-based decision for text classification
title_fullStr	An improved associative classification model using fuzzy parameterized soft set-based decision for text classification
title_full_unstemmed	An improved associative classification model using fuzzy parameterized soft set-based decision for text classification
title_sort	improved associative classification model using fuzzy parameterized soft set-based decision for text classification
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2023
url	http://eprints.uthm.edu.my/10825/1/24p%20DEDE%20ROHIDIN.pdf http://eprints.uthm.edu.my/10825/2/DEDE%20ROHIDIN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/10825/3/DEDE%20ROHIDIN%20WATERMARK.pdf
_version_	1804890120823767040

An improved associative classification model using fuzzy parameterized soft set-based decision for text classification

Similar Items