1. Gene selection for enhanced classification on microarray data using a weighted k-NN based algorithm.
- Author
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Ventura-Molina, Elías, Alarcón-Paredes, Antonio, Aldape-Pérez, Mario, Yáñez-Márquez, Cornelio, and Adolfo Alonso, Gustavo
- Subjects
GENE expression ,MACHINE learning ,DATA mining ,SUPPORT vector machines ,BIG data - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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