The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification

A new emerging nature-inspired algorithm named Bacterial Foraging Optimisation Algorithm (BFOA) that mimics the foraging behaviour of E. coli bacteria has drawn lots of attention from other researchers due to its high convergence rate and global search capability compared to others. Technically, BFO...

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Main Author:	Faizol, Bin Mohd Suria
Format:	Thesis
Language:	English
Published:	2020
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://ir.unimas.my/id/eprint/30023/1/The%20Bacterial..ft.pdf
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id	my-unimas-ir.30023
record_format	uketd_dc
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Faizol, Bin Mohd Suria The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification
description	A new emerging nature-inspired algorithm named Bacterial Foraging Optimisation Algorithm (BFOA) that mimics the foraging behaviour of E. coli bacteria has drawn lots of attention from other researchers due to its high convergence rate and global search capability compared to others. Technically, BFOA has been applied as supplementary algorithm for optimizing weight, parameters for other classifier algorithms and selecting optimised features for other classifiers. However, none of the available works had proposed BFOA as a classification algorithm despite of its good performance. Thus, this study aims to adopt and modify the BFOA into Instance Selection (IS) classifier by manipulating its global search capability and high convergence rate for data classification problem. There are two modified instance-based classifiers of BFOA were developed in this study. Both classifiers are inspired based on Prototype Selection (PS) and Prototype Generation (PG) known as BFOA-S and BFOA-G respectively. BFOA-G statistically outperformed BFOA-S by achieving 80.73% in the average testing accuracy with 5.16% average storage requirement, and 3 times faster in term of time complexity against BFOA-S in the benchmark experiment using 42 datasets. In addition, BFOA-G also performed well against ten existing IS algorithms by obtaining 83.1% in the average accuracy with 95.51% reduction rate and ranked first in the comparison study. On the other hand, BFOA-S showed competitive performance in the comparison against ten existing IS algorithms by obtaining 96.25% in reduction rate and ranked third in the ranking. Therefore, we conclude that the proposed BFOA-G is the best algorithm in the study, and PG approach is recommended for further development of BFOA as an IS algorithm.
format	Thesis
qualification_level	Master's degree
author	Faizol, Bin Mohd Suria
author_facet	Faizol, Bin Mohd Suria
author_sort	Faizol, Bin Mohd Suria
title	The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification
title_short	The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification
title_full	The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification
title_fullStr	The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification
title_full_unstemmed	The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification
title_sort	bacterial foraging optimisation algorithm using prototype selection and prototype generation for data classification
granting_institution	Universiti Malaysia Sarawak (UNIMAS)
granting_department	Faculty of Computer Science and Information Technology
publishDate	2020
url	http://ir.unimas.my/id/eprint/30023/1/The%20Bacterial..ft.pdf
_version_	1783728375045554176
spelling	my-unimas-ir.300232023-03-14T07:15:13Z The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification 2020-06-22 Faizol, Bin Mohd Suria QA75 Electronic computers. Computer science A new emerging nature-inspired algorithm named Bacterial Foraging Optimisation Algorithm (BFOA) that mimics the foraging behaviour of E. coli bacteria has drawn lots of attention from other researchers due to its high convergence rate and global search capability compared to others. Technically, BFOA has been applied as supplementary algorithm for optimizing weight, parameters for other classifier algorithms and selecting optimised features for other classifiers. However, none of the available works had proposed BFOA as a classification algorithm despite of its good performance. Thus, this study aims to adopt and modify the BFOA into Instance Selection (IS) classifier by manipulating its global search capability and high convergence rate for data classification problem. There are two modified instance-based classifiers of BFOA were developed in this study. Both classifiers are inspired based on Prototype Selection (PS) and Prototype Generation (PG) known as BFOA-S and BFOA-G respectively. BFOA-G statistically outperformed BFOA-S by achieving 80.73% in the average testing accuracy with 5.16% average storage requirement, and 3 times faster in term of time complexity against BFOA-S in the benchmark experiment using 42 datasets. In addition, BFOA-G also performed well against ten existing IS algorithms by obtaining 83.1% in the average accuracy with 95.51% reduction rate and ranked first in the comparison study. On the other hand, BFOA-S showed competitive performance in the comparison against ten existing IS algorithms by obtaining 96.25% in reduction rate and ranked third in the ranking. Therefore, we conclude that the proposed BFOA-G is the best algorithm in the study, and PG approach is recommended for further development of BFOA as an IS algorithm. Universiti Malaysia Sarawak, (UNIMAS) 2020-06 Thesis http://ir.unimas.my/id/eprint/30023/ http://ir.unimas.my/id/eprint/30023/1/The%20Bacterial..ft.pdf text en validuser masters Universiti Malaysia Sarawak (UNIMAS) Faculty of Computer Science and Information Technology RENCES Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based Learning Algorithms. Machine Learning, 6(1), 37-66. Ahmad, S. S. S. (2014, December). Feature and Instances Selection for Nearest Neighbor Classification via Cooperative PSO. In 2014 4th World Congress on Information and Communication Technologies, 45-50. AL-Hadi, I. A. A., Hashim, S. Z. M., & Shamsuddin, S. 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The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification

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