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Weighted L1-norm Logistic Regression for Gene Selection of Microarray Gene Expression Classification

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@article{IJASEIT10907,
   author = {Aiedh Mrisi Alharthi and Muhammad Hisyam Lee and Zakariya Yahya Algamal},
   title = {Weighted L1-norm Logistic Regression for Gene Selection of Microarray Gene Expression Classification},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {4},
   year = {2020},
   pages = {1483--1488},
   keywords = {lasso; adaptive lasso; logistic regression; classification; weighted lasso.},
   abstract = {

The classification of cancer is a significant application of the DNA microarray data. Gene selection methods are ordinarily used handle the issue of high-dimensionality of microarray data to enable experts to diagnose and classify cancer with high accuracy. The penalized logistic regression (PLR) technique is usually used in the dimensionality reduction of the high-dimensional gene expression data sets to remove irrelevant and redundant predictors from the binary logistic regression model. One of the regularization techniques used to achieve this goal is the least absolute shrinkage and selection operator (Lasso). However, this technique has been criticized for being biased in the selection of genes. The adaptive Lasso was usually proposed by assigning an initial weight to each gene to address the selection bias. This paper is concerned with adapting PLR to improve its capability in classification and gene selection, in the sense of accuracy, by introducing the one-dimensional weighted Mahalanobis distance (1-DWM) for each gene as an initial weight inside L1-norm. By experiments, this proposed method, denoted by adaptive penalized logistic regression (APLR), gives more accurate results compared with other famous methods in this regard.  The proposed method is applied to some real high-dimensional gene expression data sets in order to demonstrate its efficiency in terms of classification accuracy and selection of gene. Therefore, the proposed method could be utilized in other studies implementing gene selection in the area of classification of high dimensional cancer data sets.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10907},    doi = {10.18517/ijaseit.10.4.10907} }

EndNote

%A Alharthi, Aiedh Mrisi
%A Lee, Muhammad Hisyam
%A Algamal, Zakariya Yahya
%D 2020
%T Weighted L1-norm Logistic Regression for Gene Selection of Microarray Gene Expression Classification
%B 2020
%9 lasso; adaptive lasso; logistic regression; classification; weighted lasso.
%! Weighted L1-norm Logistic Regression for Gene Selection of Microarray Gene Expression Classification
%K lasso; adaptive lasso; logistic regression; classification; weighted lasso.
%X 

The classification of cancer is a significant application of the DNA microarray data. Gene selection methods are ordinarily used handle the issue of high-dimensionality of microarray data to enable experts to diagnose and classify cancer with high accuracy. The penalized logistic regression (PLR) technique is usually used in the dimensionality reduction of the high-dimensional gene expression data sets to remove irrelevant and redundant predictors from the binary logistic regression model. One of the regularization techniques used to achieve this goal is the least absolute shrinkage and selection operator (Lasso). However, this technique has been criticized for being biased in the selection of genes. The adaptive Lasso was usually proposed by assigning an initial weight to each gene to address the selection bias. This paper is concerned with adapting PLR to improve its capability in classification and gene selection, in the sense of accuracy, by introducing the one-dimensional weighted Mahalanobis distance (1-DWM) for each gene as an initial weight inside L1-norm. By experiments, this proposed method, denoted by adaptive penalized logistic regression (APLR), gives more accurate results compared with other famous methods in this regard.  The proposed method is applied to some real high-dimensional gene expression data sets in order to demonstrate its efficiency in terms of classification accuracy and selection of gene. Therefore, the proposed method could be utilized in other studies implementing gene selection in the area of classification of high dimensional cancer data sets.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10907 %R doi:10.18517/ijaseit.10.4.10907 %J International Journal on Advanced Science, Engineering and Information Technology %V 10 %N 4 %@ 2088-5334

IEEE

Aiedh Mrisi Alharthi,Muhammad Hisyam Lee and Zakariya Yahya Algamal,"Weighted L1-norm Logistic Regression for Gene Selection of Microarray Gene Expression Classification," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 4, pp. 1483-1488, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.4.10907.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Alharthi, Aiedh Mrisi
AU  - Lee, Muhammad Hisyam
AU  - Algamal, Zakariya Yahya
PY  - 2020
TI  - Weighted L1-norm Logistic Regression for Gene Selection of Microarray Gene Expression Classification
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 4
Y2  - 2020
SP  - 1483
EP  - 1488
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - lasso; adaptive lasso; logistic regression; classification; weighted lasso.
N2  - 

The classification of cancer is a significant application of the DNA microarray data. Gene selection methods are ordinarily used handle the issue of high-dimensionality of microarray data to enable experts to diagnose and classify cancer with high accuracy. The penalized logistic regression (PLR) technique is usually used in the dimensionality reduction of the high-dimensional gene expression data sets to remove irrelevant and redundant predictors from the binary logistic regression model. One of the regularization techniques used to achieve this goal is the least absolute shrinkage and selection operator (Lasso). However, this technique has been criticized for being biased in the selection of genes. The adaptive Lasso was usually proposed by assigning an initial weight to each gene to address the selection bias. This paper is concerned with adapting PLR to improve its capability in classification and gene selection, in the sense of accuracy, by introducing the one-dimensional weighted Mahalanobis distance (1-DWM) for each gene as an initial weight inside L1-norm. By experiments, this proposed method, denoted by adaptive penalized logistic regression (APLR), gives more accurate results compared with other famous methods in this regard.  The proposed method is applied to some real high-dimensional gene expression data sets in order to demonstrate its efficiency in terms of classification accuracy and selection of gene. Therefore, the proposed method could be utilized in other studies implementing gene selection in the area of classification of high dimensional cancer data sets.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10907 DO - 10.18517/ijaseit.10.4.10907

RefWorks

RT Journal Article
ID 10907
A1 Alharthi, Aiedh Mrisi
A1 Lee, Muhammad Hisyam
A1 Algamal, Zakariya Yahya
T1 Weighted L1-norm Logistic Regression for Gene Selection of Microarray Gene Expression Classification
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 4
YR 2020
SP 1483
OP 1488
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 lasso; adaptive lasso; logistic regression; classification; weighted lasso.
AB 

The classification of cancer is a significant application of the DNA microarray data. Gene selection methods are ordinarily used handle the issue of high-dimensionality of microarray data to enable experts to diagnose and classify cancer with high accuracy. The penalized logistic regression (PLR) technique is usually used in the dimensionality reduction of the high-dimensional gene expression data sets to remove irrelevant and redundant predictors from the binary logistic regression model. One of the regularization techniques used to achieve this goal is the least absolute shrinkage and selection operator (Lasso). However, this technique has been criticized for being biased in the selection of genes. The adaptive Lasso was usually proposed by assigning an initial weight to each gene to address the selection bias. This paper is concerned with adapting PLR to improve its capability in classification and gene selection, in the sense of accuracy, by introducing the one-dimensional weighted Mahalanobis distance (1-DWM) for each gene as an initial weight inside L1-norm. By experiments, this proposed method, denoted by adaptive penalized logistic regression (APLR), gives more accurate results compared with other famous methods in this regard.  The proposed method is applied to some real high-dimensional gene expression data sets in order to demonstrate its efficiency in terms of classification accuracy and selection of gene. Therefore, the proposed method could be utilized in other studies implementing gene selection in the area of classification of high dimensional cancer data sets.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10907 DO - 10.18517/ijaseit.10.4.10907