Name
Abstract | ||
|---|---|---|
Normally the microarray data contain a large number of genes (usually more than 1000) and a relatively small number of samples (usually fewer than 100). This makes the discriminant analysis of DNA microarray data hard to handle. Selecting important genes to the discriminant problem is hence of much practically significance in microarray data analysis. If put in the context of pattern classification, gene selection can be casted as a feature selection problem. Feature selection approaches are broadly grouped into filter and wrapper methods. The wrapper method outperforms the filter method in general. However the accuracy of wrapper methods is coupled with intensive computations. In present study, we proposed a wrapper-based gene selection algorithm by employing the Regularization Network as the classifier. Compared with classical wrapper method, the computational costs in our gene selection algorithm is significantly reduced, because the evaluation criterion we used does not demand repeated trainings in the leave-one-out procedure. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11508069_54 | IDEAL |
Keywords | Field | DocType |
regularization network,wrapper-based gene selection algorithm,filter method,feature selection problem,important gene,classical wrapper method,gene selection,wrapper method,feature selection approach,dna microarray data,gene selection algorithm,discriminant analysis,microarray data analysis,feature selection,microarray data | Small number,Data mining,Pattern recognition,Feature selection,Discriminant,Computer science,Support vector machine,Information extraction,Artificial intelligence,Linear discriminant analysis,Classifier (linguistics),DNA microarray | Conference |
Volume | ISSN | ISBN |
3578 | 0302-9743 | 3-540-26972-X |
Citations | PageRank | References |
2 | 0.45 | 4 |
Authors | ||
2 | ||