TY - JOUR
T1 - Gene selection for enhanced classification on microarray data using a weighted k-NN based algorithm
AU - Ventura-Molina, Elías
AU - Alarcón-Paredes, Antonio
AU - Aldape-Pérez, Mario
AU - Yáñez-Márquez, Cornelio
AU - Adolfo Alonso, Gustavo
N1 - Publisher Copyright:
© 2019 - IOS Press and the authors. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Feature selection is a common solution to microarray analysis. Previous approaches either select features based on classical statistical tests that can be tuned up with a classifier, or using regularization penalties incorporated in the cost function. Here we propose to use a feature ranking and weighting scheme instead, which combines statistical techniques with a weighted k-NN classifier using a modified forward selection procedure. We demonstrate that classification accuracy of our proposal outperforms existing methods on a range of public microarray gene expression datasets. The proposed method is also compared to state-of-the-art feature selection algorithms by means of the Friedman test. Although a bunch of feature selection techniques has been used for genomic data, the experimental results show the classification superiority of our method on most of the present gene expression datasets.
AB - Feature selection is a common solution to microarray analysis. Previous approaches either select features based on classical statistical tests that can be tuned up with a classifier, or using regularization penalties incorporated in the cost function. Here we propose to use a feature ranking and weighting scheme instead, which combines statistical techniques with a weighted k-NN classifier using a modified forward selection procedure. We demonstrate that classification accuracy of our proposal outperforms existing methods on a range of public microarray gene expression datasets. The proposed method is also compared to state-of-the-art feature selection algorithms by means of the Friedman test. Although a bunch of feature selection techniques has been used for genomic data, the experimental results show the classification superiority of our method on most of the present gene expression datasets.
KW - Computational genomics
KW - feature ranking
KW - feature selection
KW - feature weighting
KW - k-nearest neighbors
KW - microarray data analysis
UR - http://www.scopus.com/inward/record.url?scp=85062208436&partnerID=8YFLogxK
U2 - 10.3233/IDA-173720
DO - 10.3233/IDA-173720
M3 - Artículo
SN - 1088-467X
VL - 23
SP - 241
EP - 253
JO - Intelligent Data Analysis
JF - Intelligent Data Analysis
IS - 1
ER -