An image feature selection approach for dimensionality reduction based on kNN and SVM for AkT proteins

Communication triggered for cell survival/apoptosis is achieved by three different input proteins. In this paper, we have considered the heat map image that shows 11 different proteins for the HT carcinoma cells which helps in cell survival/apoptosis. Based on the introduction and integration of an...

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Autores principales:	Shruti Jain, Ayodeji Olalekan Salau
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2019
Materias:	feature selection wrapper method filter method classification akt Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/7730367c7bfc4d678ff0e5a6c89ca3cf
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spelling	oai:doaj.org-article:7730367c7bfc4d678ff0e5a6c89ca3cf2021-11-04T15:51:55ZAn image feature selection approach for dimensionality reduction based on kNN and SVM for AkT proteins2331-191610.1080/23311916.2019.1599537https://doaj.org/article/7730367c7bfc4d678ff0e5a6c89ca3cf2019-01-01T00:00:00Zhttp://dx.doi.org/10.1080/23311916.2019.1599537https://doaj.org/toc/2331-1916Communication triggered for cell survival/apoptosis is achieved by three different input proteins. In this paper, we have considered the heat map image that shows 11 different proteins for the HT carcinoma cells which helps in cell survival/apoptosis. Based on the introduction and integration of an algorithm in the classification model, feature selection was divided into three main categories namely: filtering method (FM), wrapper method (WM), and Embedded Method (EM). After applying the feature selection (FS) algorithm, we obtained 7 different marker proteins but out of these proteins, this paper concentrates on only one of them, the AkT which is used for classification using k-nearest neighbour (kNN) classifier and support vector machine (SVM) classifier for calculating predicted mean, standard deviation ratio, and correlation. For kNN, we have used different distance approaches (Euclidean, city block), while for SVM, linear, polynomial, RBF and sigmoid kernels are used for Tier 1 and Tier 2. Results with linear Tier 1 using SVM and Euclidean distance outperform other methods. An accuracy of 76.9% and 84.6% was obtained using the kNN and SVM classifiers respectively with GLDS features. The results obtained gave a better performance when compared with the result of other research papers.Shruti JainAyodeji Olalekan SalauTaylor & Francis Grouparticlefeature selectionwrapper methodfilter methodclassificationaktEngineering (General). Civil engineering (General)TA1-2040ENCogent Engineering, Vol 6, Iss 1 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	feature selection wrapper method filter method classification akt Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	feature selection wrapper method filter method classification akt Engineering (General). Civil engineering (General) TA1-2040 Shruti Jain Ayodeji Olalekan Salau An image feature selection approach for dimensionality reduction based on kNN and SVM for AkT proteins
description	Communication triggered for cell survival/apoptosis is achieved by three different input proteins. In this paper, we have considered the heat map image that shows 11 different proteins for the HT carcinoma cells which helps in cell survival/apoptosis. Based on the introduction and integration of an algorithm in the classification model, feature selection was divided into three main categories namely: filtering method (FM), wrapper method (WM), and Embedded Method (EM). After applying the feature selection (FS) algorithm, we obtained 7 different marker proteins but out of these proteins, this paper concentrates on only one of them, the AkT which is used for classification using k-nearest neighbour (kNN) classifier and support vector machine (SVM) classifier for calculating predicted mean, standard deviation ratio, and correlation. For kNN, we have used different distance approaches (Euclidean, city block), while for SVM, linear, polynomial, RBF and sigmoid kernels are used for Tier 1 and Tier 2. Results with linear Tier 1 using SVM and Euclidean distance outperform other methods. An accuracy of 76.9% and 84.6% was obtained using the kNN and SVM classifiers respectively with GLDS features. The results obtained gave a better performance when compared with the result of other research papers.
format	article
author	Shruti Jain Ayodeji Olalekan Salau
author_facet	Shruti Jain Ayodeji Olalekan Salau
author_sort	Shruti Jain
title	An image feature selection approach for dimensionality reduction based on kNN and SVM for AkT proteins
title_short	An image feature selection approach for dimensionality reduction based on kNN and SVM for AkT proteins
title_full	An image feature selection approach for dimensionality reduction based on kNN and SVM for AkT proteins
title_fullStr	An image feature selection approach for dimensionality reduction based on kNN and SVM for AkT proteins
title_full_unstemmed	An image feature selection approach for dimensionality reduction based on kNN and SVM for AkT proteins
title_sort	image feature selection approach for dimensionality reduction based on knn and svm for akt proteins
publisher	Taylor & Francis Group
publishDate	2019
url	https://doaj.org/article/7730367c7bfc4d678ff0e5a6c89ca3cf
work_keys_str_mv	AT shrutijain animagefeatureselectionapproachfordimensionalityreductionbasedonknnandsvmforaktproteins AT ayodejiolalekansalau animagefeatureselectionapproachfordimensionalityreductionbasedonknnandsvmforaktproteins AT shrutijain imagefeatureselectionapproachfordimensionalityreductionbasedonknnandsvmforaktproteins AT ayodejiolalekansalau imagefeatureselectionapproachfordimensionalityreductionbasedonknnandsvmforaktproteins
_version_	1718444720460398592

An image feature selection approach for dimensionality reduction based on kNN and SVM for AkT proteins

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