ESTIMATION OF MISSING VALUES USING OPTIMISED HYBRID FUZZY C-MEANS AND MAJORITY VOTE FOR MICROARRAY DATA

Missing values are a huge constraint in microarray technologies towards improving and identifying disease-causing genes. Estimating missing values is an undeniable scenario faced by field experts. The imputation method is an effective way to impute the proper values to proceed with the next process...

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Autores principales:	Shamini Raja Kumaran, Mohd Shahizan Othman, Lizawati Mi Yusuf
Formato:	article
Lenguaje:	EN
Publicado:	UUM Press 2020
Materias:	fuzzy c-means majority vote missing values microarray data data optimisation Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/f49774a11c4548b4b36183a8c53a4532
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spelling	oai:doaj.org-article:f49774a11c4548b4b36183a8c53a45322021-11-15T07:17:02ZESTIMATION OF MISSING VALUES USING OPTIMISED HYBRID FUZZY C-MEANS AND MAJORITY VOTE FOR MICROARRAY DATA10.32890/jict2020.19.4.11675-414X2180-3862https://doaj.org/article/f49774a11c4548b4b36183a8c53a45322020-08-01T00:00:00Zhttp://e-journal.uum.edu.my/index.php/jict/article/view/jict2020.19.4.1https://doaj.org/toc/1675-414Xhttps://doaj.org/toc/2180-3862Missing values are a huge constraint in microarray technologies towards improving and identifying disease-causing genes. Estimating missing values is an undeniable scenario faced by field experts. The imputation method is an effective way to impute the proper values to proceed with the next process in microarray technology. Missing value imputation methods may increase the classification accuracy. Although these methods might predict the values, classification accuracy rates prove the ability of the methods to identify the missing values in gene expression data. In this study, a novel method, Optimised Hybrid of Fuzzy C-Means and Majority Vote (opt-FCMMV), was proposed to identify the missing values in the data. Using the Majority Vote (MV) and optimisation through Particle Swarm Optimisation (PSO), this study predicted missing values in the data to form more informative and solid data. In order to verify the effectiveness of opt-FCMMV, several experiments were carried out on two publicly available microarray datasets (i.e. Ovary and Lung Cancer) under three missing value mechanisms with five different percentage values in the biomedical domain using Support Vector Machine (SVM) classifier. The experimental results showed that the proposed method functioned efficiently by showcasing the highest accuracy rate as compared to the one without imputations, with imputation by Fuzzy C-Means (FCM), and imputation by Fuzzy C-Means with Majority Vote (FCMMV). For example, the accuracy rates for Ovary Cancer data with 5% missing values were 64.0% for no imputation, 81.8% (FCM), 90.0% (FCMMV), and 93.7% (opt-FCMMV). Such an outcome indicates that the opt-FCMMV may also be applied in different domains in order to prepare the dataset for various data mining tasks. Shamini Raja KumaranMohd Shahizan OthmanLizawati Mi YusufUUM Pressarticlefuzzy c-meansmajority votemissing valuesmicroarray datadata optimisationInformation technologyT58.5-58.64ENJournal of ICT, Vol 19, Iss 4, Pp 459-482 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	fuzzy c-means majority vote missing values microarray data data optimisation Information technology T58.5-58.64
spellingShingle	fuzzy c-means majority vote missing values microarray data data optimisation Information technology T58.5-58.64 Shamini Raja Kumaran Mohd Shahizan Othman Lizawati Mi Yusuf ESTIMATION OF MISSING VALUES USING OPTIMISED HYBRID FUZZY C-MEANS AND MAJORITY VOTE FOR MICROARRAY DATA
description	Missing values are a huge constraint in microarray technologies towards improving and identifying disease-causing genes. Estimating missing values is an undeniable scenario faced by field experts. The imputation method is an effective way to impute the proper values to proceed with the next process in microarray technology. Missing value imputation methods may increase the classification accuracy. Although these methods might predict the values, classification accuracy rates prove the ability of the methods to identify the missing values in gene expression data. In this study, a novel method, Optimised Hybrid of Fuzzy C-Means and Majority Vote (opt-FCMMV), was proposed to identify the missing values in the data. Using the Majority Vote (MV) and optimisation through Particle Swarm Optimisation (PSO), this study predicted missing values in the data to form more informative and solid data. In order to verify the effectiveness of opt-FCMMV, several experiments were carried out on two publicly available microarray datasets (i.e. Ovary and Lung Cancer) under three missing value mechanisms with five different percentage values in the biomedical domain using Support Vector Machine (SVM) classifier. The experimental results showed that the proposed method functioned efficiently by showcasing the highest accuracy rate as compared to the one without imputations, with imputation by Fuzzy C-Means (FCM), and imputation by Fuzzy C-Means with Majority Vote (FCMMV). For example, the accuracy rates for Ovary Cancer data with 5% missing values were 64.0% for no imputation, 81.8% (FCM), 90.0% (FCMMV), and 93.7% (opt-FCMMV). Such an outcome indicates that the opt-FCMMV may also be applied in different domains in order to prepare the dataset for various data mining tasks.
format	article
author	Shamini Raja Kumaran Mohd Shahizan Othman Lizawati Mi Yusuf
author_facet	Shamini Raja Kumaran Mohd Shahizan Othman Lizawati Mi Yusuf
author_sort	Shamini Raja Kumaran
title	ESTIMATION OF MISSING VALUES USING OPTIMISED HYBRID FUZZY C-MEANS AND MAJORITY VOTE FOR MICROARRAY DATA
title_short	ESTIMATION OF MISSING VALUES USING OPTIMISED HYBRID FUZZY C-MEANS AND MAJORITY VOTE FOR MICROARRAY DATA
title_full	ESTIMATION OF MISSING VALUES USING OPTIMISED HYBRID FUZZY C-MEANS AND MAJORITY VOTE FOR MICROARRAY DATA
title_fullStr	ESTIMATION OF MISSING VALUES USING OPTIMISED HYBRID FUZZY C-MEANS AND MAJORITY VOTE FOR MICROARRAY DATA
title_full_unstemmed	ESTIMATION OF MISSING VALUES USING OPTIMISED HYBRID FUZZY C-MEANS AND MAJORITY VOTE FOR MICROARRAY DATA
title_sort	estimation of missing values using optimised hybrid fuzzy c-means and majority vote for microarray data
publisher	UUM Press
publishDate	2020
url	https://doaj.org/article/f49774a11c4548b4b36183a8c53a4532
work_keys_str_mv	AT shaminirajakumaran estimationofmissingvaluesusingoptimisedhybridfuzzycmeansandmajorityvoteformicroarraydata AT mohdshahizanothman estimationofmissingvaluesusingoptimisedhybridfuzzycmeansandmajorityvoteformicroarraydata AT lizawatimiyusuf estimationofmissingvaluesusingoptimisedhybridfuzzycmeansandmajorityvoteformicroarraydata
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ESTIMATION OF MISSING VALUES USING OPTIMISED HYBRID FUZZY C-MEANS AND MAJORITY VOTE FOR MICROARRAY DATA

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