| Título |
Gene selection for enhanced classification on microarray data using a weighted k-NN based algorithm |
| Tipo |
Revista |
| Sub-tipo |
JCR |
| Descripción |
Intelligent Data Analysis |
| Resumen |
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. |
| Observaciones |
JCR Q4,
DOI 10.3233/IDA-173720
https://content.iospress.com/articles/intelligent-data-analysis/ida173720 |
| Lugar |
Amsterdam |
| País |
Paises Bajos |
| No. de páginas |
241-253 |
| Vol. / Cap. |
v. 23 no. 1 |
| Inicio |
2019-02-19 |
| Fin |
|
| ISBN/ISSN |
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