Name
Abstract | ||
|---|---|---|
Microarray data classification is a critical challenge for computational techniques due to its inherent characteristics, mainly small sample size and high dimension of the input space. For this type of data two-class classification techniques have been widely applied while one-class learning is considered as a promising approach. In this paper, we study the suitability of employing the one-class classification for microarray datasets while the role played by feature selection is analyzed. The superiority of this approach is demonstrated by comparison with the classical approach, with two classes, on different benchmark data sets. |
Year | Venue | Field |
|---|---|---|
2015 | EANN | Data set,Microarray,One-class classification,Feature selection,Pattern recognition,Computer science,Microarray analysis techniques,Artificial intelligence,Microarray databases,Sample size determination |
DocType | Citations | PageRank |
Conference | 1 | 0.36 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Beatriz Pérez-Sánchez | 1 | 95 | 14.03 |
| Oscar Fontenla-Romero | 2 | 337 | 39.49 |
| Noelia Sánchez-Maroño | 3 | 406 | 25.39 |