| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 409552 | Neurocomputing | 2006 | 4 Pages |
Abstract
Predicting the different levels of gastric carcinoma from clinical and histopathological investigations is an important problem in bioinformatics and a challenging task for machine learning algorithms. In this paper, we have investigated an ensemble of classifiers and tested it on a real-world dataset. A genetic algorithm is applied to select the most relevant features. The obtained results are very encouraging, our results improve the average predictive accuracy obtained in previously published works.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Loris Nanni,