Mutable composite firefly algorithm for gene selection in microarray based cancer classification

Cancer classification is critical due to the strenuous effort required in cancer treatment and the rising cancer mortality rate. Recent trends with high throughput technologies have led to discoveries in terms of biomarkers that successfully contributed to cancerrelated issues. A computational appro...

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Main Author:	Fajila, Mohamed Nisper Fathima
Format:	Thesis
Language:	eng eng
Published:	2022
Subjects:	RC0254 Neoplasms Tumors Oncology (including Cancer)
Online Access:	https://etd.uum.edu.my/10171/1/s826643_01.pdf https://etd.uum.edu.my/10171/2/s826643_02.pdf
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id	my-uum-etd.10171
record_format	uketd_dc
spelling	my-uum-etd.101712022-12-19T09:56:59Z Mutable composite firefly algorithm for gene selection in microarray based cancer classification 2022 Fajila, Mohamed Nisper Fathima Yusof, Yuhanis Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences RC0254 Neoplasms. Tumors. Oncology (including Cancer) Cancer classification is critical due to the strenuous effort required in cancer treatment and the rising cancer mortality rate. Recent trends with high throughput technologies have led to discoveries in terms of biomarkers that successfully contributed to cancerrelated issues. A computational approach for gene selection based on microarray data analysis has been applied in many cancer classification problems. However, the existing hybrid approaches with metaheuristic optimization algorithms in feature selection (specifically in gene selection) are not generalized enough to efficiently classify most cancer microarray data while maintaining a small set of genes. This leads to the classification accuracy and genes subset size problem. Hence, this study proposed to modify the Firefly Algorithm (FA) along with the Correlation-based Feature Selection (CFS) filter for the gene selection task. An improved FA was proposed to overcome FA slow convergence by generating mutable size solutions for the firefly population. In addition, a composite position update strategy was designed for the mutable size solutions. The proposed strategy was to balance FA exploration and exploitation in order to address the local optima problem. The proposed hybrid algorithm known as CFS-Mutable Composite Firefly Algorithm (CFS-MCFA) was evaluated on cancer microarray data for biomarker selection along with the deployment of Support Vector Machine (SVM) as the classifier. Evaluation was performed based on two metrics: classification accuracy and size of feature set. The results showed that the CFS-MCFA-SVM algorithm outperforms benchmark methods in terms of classification accuracy and genes subset size. In particular, 100 percent accuracy was achieved on all four datasets and with only a few biomarkers (between one and four). This result indicates that the proposed algorithm is one of the competitive alternatives in feature selection, which later contributes to the analysis of microarray data. 2022 Thesis https://etd.uum.edu.my/10171/ https://etd.uum.edu.my/10171/1/s826643_01.pdf text eng 2025-04-07 staffonly https://etd.uum.edu.my/10171/2/s826643_02.pdf text eng public other masters Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Yusof, Yuhanis
topic	RC0254 Neoplasms Tumors Oncology (including Cancer)
spellingShingle	RC0254 Neoplasms Tumors Oncology (including Cancer) Fajila, Mohamed Nisper Fathima Mutable composite firefly algorithm for gene selection in microarray based cancer classification
description	Cancer classification is critical due to the strenuous effort required in cancer treatment and the rising cancer mortality rate. Recent trends with high throughput technologies have led to discoveries in terms of biomarkers that successfully contributed to cancerrelated issues. A computational approach for gene selection based on microarray data analysis has been applied in many cancer classification problems. However, the existing hybrid approaches with metaheuristic optimization algorithms in feature selection (specifically in gene selection) are not generalized enough to efficiently classify most cancer microarray data while maintaining a small set of genes. This leads to the classification accuracy and genes subset size problem. Hence, this study proposed to modify the Firefly Algorithm (FA) along with the Correlation-based Feature Selection (CFS) filter for the gene selection task. An improved FA was proposed to overcome FA slow convergence by generating mutable size solutions for the firefly population. In addition, a composite position update strategy was designed for the mutable size solutions. The proposed strategy was to balance FA exploration and exploitation in order to address the local optima problem. The proposed hybrid algorithm known as CFS-Mutable Composite Firefly Algorithm (CFS-MCFA) was evaluated on cancer microarray data for biomarker selection along with the deployment of Support Vector Machine (SVM) as the classifier. Evaluation was performed based on two metrics: classification accuracy and size of feature set. The results showed that the CFS-MCFA-SVM algorithm outperforms benchmark methods in terms of classification accuracy and genes subset size. In particular, 100 percent accuracy was achieved on all four datasets and with only a few biomarkers (between one and four). This result indicates that the proposed algorithm is one of the competitive alternatives in feature selection, which later contributes to the analysis of microarray data.
format	Thesis
qualification_name	other
qualification_level	Master's degree
author	Fajila, Mohamed Nisper Fathima
author_facet	Fajila, Mohamed Nisper Fathima
author_sort	Fajila, Mohamed Nisper Fathima
title	Mutable composite firefly algorithm for gene selection in microarray based cancer classification
title_short	Mutable composite firefly algorithm for gene selection in microarray based cancer classification
title_full	Mutable composite firefly algorithm for gene selection in microarray based cancer classification
title_fullStr	Mutable composite firefly algorithm for gene selection in microarray based cancer classification
title_full_unstemmed	Mutable composite firefly algorithm for gene selection in microarray based cancer classification
title_sort	mutable composite firefly algorithm for gene selection in microarray based cancer classification
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2022
url	https://etd.uum.edu.my/10171/1/s826643_01.pdf https://etd.uum.edu.my/10171/2/s826643_02.pdf
_version_	1776103757613891584

Mutable composite firefly algorithm for gene selection in microarray based cancer classification

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