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Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree

RNA-Seq data are utilized for biological applications and decision making for classification of genes. Lots of work in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in fetching relevant information in a given data. In this s...

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Main Authors: Arowolo, Micheal Olaolu, Adebiyi, Marion Olubunmi, Adebiyi, Ayodele Ariyo
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2021
Online Access:http://journalarticle.ukm.my/18056/1/7.pdf
http://journalarticle.ukm.my/18056/
https://www.ukm.my/jsm/malay_journals/jilid50bil9_2021/KandunganJilid50Bil9_2021.html
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spelling my-ukm.journal.180562022-02-18T00:41:18Z http://journalarticle.ukm.my/18056/ Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree Arowolo, Micheal Olaolu Adebiyi, Marion Olubunmi Adebiyi, Ayodele Ariyo RNA-Seq data are utilized for biological applications and decision making for classification of genes. Lots of work in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in fetching relevant information in a given data. In this study, a novel optimized dimensionality reduction algorithm is proposed, by combining an optimized genetic algorithm with Principal Component Analysis and Independent Component Analysis (GA-O-PCA and GAO-ICA), which are used to identify an optimum subset and latent correlated features, respectively. The classifier uses Decision tree on the reduced mosquito anopheles gambiae dataset to enhance the accuracy and scalability in the gene expression analysis. The proposed algorithm is used to fetch relevant features based from the high-dimensional input feature space. A feature ranking and earlier experience are used. The performances of the model are evaluated and validated using the classification accuracy to compare existing approaches in the literature. The achieved experimental results prove to be promising for feature selection and classification in gene expression data analysis and specify that the approach is a capable accumulation to prevailing data mining techniques. Penerbit Universiti Kebangsaan Malaysia 2021-09 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/18056/1/7.pdf Arowolo, Micheal Olaolu and Adebiyi, Marion Olubunmi and Adebiyi, Ayodele Ariyo (2021) Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree. Sains Malaysiana, 50 (9). pp. 2579-2589. ISSN 0126-6039 https://www.ukm.my/jsm/malay_journals/jilid50bil9_2021/KandunganJilid50Bil9_2021.html
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description RNA-Seq data are utilized for biological applications and decision making for classification of genes. Lots of work in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in fetching relevant information in a given data. In this study, a novel optimized dimensionality reduction algorithm is proposed, by combining an optimized genetic algorithm with Principal Component Analysis and Independent Component Analysis (GA-O-PCA and GAO-ICA), which are used to identify an optimum subset and latent correlated features, respectively. The classifier uses Decision tree on the reduced mosquito anopheles gambiae dataset to enhance the accuracy and scalability in the gene expression analysis. The proposed algorithm is used to fetch relevant features based from the high-dimensional input feature space. A feature ranking and earlier experience are used. The performances of the model are evaluated and validated using the classification accuracy to compare existing approaches in the literature. The achieved experimental results prove to be promising for feature selection and classification in gene expression data analysis and specify that the approach is a capable accumulation to prevailing data mining techniques.
format Article
author Arowolo, Micheal Olaolu
Adebiyi, Marion Olubunmi
Adebiyi, Ayodele Ariyo
spellingShingle Arowolo, Micheal Olaolu
Adebiyi, Marion Olubunmi
Adebiyi, Ayodele Ariyo
Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree
author_facet Arowolo, Micheal Olaolu
Adebiyi, Marion Olubunmi
Adebiyi, Ayodele Ariyo
author_sort Arowolo, Micheal Olaolu
title Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree
title_short Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree
title_full Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree
title_fullStr Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree
title_full_unstemmed Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree
title_sort enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2021
url http://journalarticle.ukm.my/18056/1/7.pdf
http://journalarticle.ukm.my/18056/
https://www.ukm.my/jsm/malay_journals/jilid50bil9_2021/KandunganJilid50Bil9_2021.html
_version_ 1725973504588775424
score 13.211869

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