Fuzzy modeling using Bat Algorithm optimization for classification

Fuzzy modeling is a process of generating parameters which are fuzzy rule and membership function. Fuzzy rule is a form of a fuzzy condition and membership function is a generality of indicator function in classical sets. In order to create parameters, there are many problems arise in the process of...

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Main Author:	Noor Amidah, Ahmad Sultan
Format:	Undergraduates Project Papers
Language:	English
Published:	2018
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/27076/ http://umpir.ump.edu.my/id/eprint/27076/ http://umpir.ump.edu.my/id/eprint/27076/1/Fuzzy%20modeling%20using%20bat%20algorithm%20optimization.pdf

id	ump-27076
recordtype	eprints
spelling	ump-270762019-12-23T02:06:04Z http://umpir.ump.edu.my/id/eprint/27076/ Fuzzy modeling using Bat Algorithm optimization for classification Noor Amidah, Ahmad Sultan QA75 Electronic computers. Computer science QA76 Computer software Fuzzy modeling is a process of generating parameters which are fuzzy rule and membership function. Fuzzy rule is a form of a fuzzy condition and membership function is a generality of indicator function in classical sets. In order to create parameters, there are many problems arise in the process of fuzzy modeling. The problems are data incomplete and the size of the data is large. Data incomplete is happened when some of data to process is missing so it failed to record it. Problem of the size of data happen when data cannot be process because of it too complex. In order to solve it, Bat Algorithm method is implement in to optimization method in fuzzy modeling for classification. The study of Bat Algorithm is achieved the purpose of this research. The purpose of this research is to apply and evaluate the performance of Bat Algorithm for classification. In order conduct an experiment in this research, there are several dataset is use which are WBCD dataset, Haberman’s Survival dataset and Pima Indian dataset. A Sazonov Engine which is a fuzzy java engine is use to apply Bat Algorithm in the experiment. The value of parameter is already set to use when applying every dataset in an experiment. As a result, the highest average accuracy generated which is 96.91% by using WBCD dataset. The average accuracy of Bat Algorithm is comparing with other methods. Every dataset is producing the best fuzzy rule and membership function. Overall of this research, the objectives of this research is achieved. 2018-12 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/27076/1/Fuzzy%20modeling%20using%20bat%20algorithm%20optimization.pdf Noor Amidah, Ahmad Sultan (2018) Fuzzy modeling using Bat Algorithm optimization for classification. Faculty of Computer System & Software Engineering, Universiti Malaysia Pahang. http://fypro.ump.edu.my/ethesis/index.php
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science QA76 Computer software
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software Noor Amidah, Ahmad Sultan Fuzzy modeling using Bat Algorithm optimization for classification
description	Fuzzy modeling is a process of generating parameters which are fuzzy rule and membership function. Fuzzy rule is a form of a fuzzy condition and membership function is a generality of indicator function in classical sets. In order to create parameters, there are many problems arise in the process of fuzzy modeling. The problems are data incomplete and the size of the data is large. Data incomplete is happened when some of data to process is missing so it failed to record it. Problem of the size of data happen when data cannot be process because of it too complex. In order to solve it, Bat Algorithm method is implement in to optimization method in fuzzy modeling for classification. The study of Bat Algorithm is achieved the purpose of this research. The purpose of this research is to apply and evaluate the performance of Bat Algorithm for classification. In order conduct an experiment in this research, there are several dataset is use which are WBCD dataset, Haberman’s Survival dataset and Pima Indian dataset. A Sazonov Engine which is a fuzzy java engine is use to apply Bat Algorithm in the experiment. The value of parameter is already set to use when applying every dataset in an experiment. As a result, the highest average accuracy generated which is 96.91% by using WBCD dataset. The average accuracy of Bat Algorithm is comparing with other methods. Every dataset is producing the best fuzzy rule and membership function. Overall of this research, the objectives of this research is achieved.
format	Undergraduates Project Papers
author	Noor Amidah, Ahmad Sultan
author_facet	Noor Amidah, Ahmad Sultan
author_sort	Noor Amidah, Ahmad Sultan
title	Fuzzy modeling using Bat Algorithm optimization for classification
title_short	Fuzzy modeling using Bat Algorithm optimization for classification
title_full	Fuzzy modeling using Bat Algorithm optimization for classification
title_fullStr	Fuzzy modeling using Bat Algorithm optimization for classification
title_full_unstemmed	Fuzzy modeling using Bat Algorithm optimization for classification
title_sort	fuzzy modeling using bat algorithm optimization for classification
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/27076/ http://umpir.ump.edu.my/id/eprint/27076/ http://umpir.ump.edu.my/id/eprint/27076/1/Fuzzy%20modeling%20using%20bat%20algorithm%20optimization.pdf
first_indexed	2023-09-18T22:42:28Z
last_indexed	2023-09-18T22:42:28Z
_version_	1777417006683783168

Fuzzy modeling using Bat Algorithm optimization for classification

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