Name
Abstract | ||
|---|---|---|
As a high dimensional problem, analysis of microarray datasets is a hard task, where many weakly relevant but redundant featureshurt generalization performance of classifiers. There are previous worksto handle this problem by using linear or nonlinear filters, but thesefilters do not consider discriminative contribution of each feature by utilizingthe label information. Here we propose a novel metric based ondiscriminative contribution to perform redundant feature elimination.By the new metric, complementary features are likely to be reserved,which is beneficial for the final classification. Experimental results onseveral microarray data sets show our proposed metric for redundantfeature elimination based on discriminative contribution is better thanthe previous state-of-arts linear or nonlinear metrics on the problem ofanalysis of microarray data sets. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-79450-9_24 | ISBRA |
Keywords | Field | DocType |
microarray datasets,redundant gene elimination,ondiscriminative contribution,nonlinear metrics,nonlinear filter,discriminative contribution,high dimensional problem,previous worksto,problem ofanalysis,complementary feature,microarray data set,microarray data | Data mining,High dimensional problem,Nonlinear system,Pattern recognition,Feature selection,Computer science,Support vector machine,Mutual information,Artificial intelligence,Discriminative model,Machine learning | Conference |
Volume | ISSN | ISBN |
4983 | 0302-9743 | 3-540-79449-2 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xue-qiang Zeng | 1 | 76 | 7.91 |
| Guo-Zheng Li | 2 | 368 | 42.62 |
| Jack Y. Yang | 3 | 902 | 175.51 |
| Mary Qu Yang | 4 | 933 | 191.35 |