Name
Abstract | ||
|---|---|---|
As a high dimensional problem, analysis of microarray data sets is a challenging task, where many weakly relevant or redundant features hurt generalization performance of classifiers. The previous works used redundant feature detection methods to select discriminative compact gene set, which only considered the relationship among features, not the redundancy of classification ability among features. Here, we propose a novel algorithm named RESI (Redundant fEature Selection depending on Instance), which considers label information in the measure of feature subset redundancy. Experimental results on benchmark data sets show that RESI performs better than the previous state-of-arts algorithms on redundant feature selection methods like mRMR. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/BIBMW.2012.6470320 | BIBM Workshops |
Keywords | Field | DocType |
microarray data,previous work,redundant feature detection,compact gene set,redundant feature,microarray data analysis,resi algorithm,supervised solution,redundant feature selection method,feature subset redundancy,instance learning,learning (artificial intelligence),genetics,feature selection,challenging task,pattern classification,data analysis,classification ability,previous state-of-arts algorithm,benchmark data set,biology computing,molecular biophysics,redundant feature detection method,classifier generalization performance,mrmr algorithm,redundant feature selection,generalisation (artificial intelligence),microarray data set,learning artificial intelligence | Data mining,Data set,Feature selection,Feature detection,Computer science,Redundancy (engineering),Artificial intelligence,Discriminative model,High dimensional problem,Pattern recognition,Feature (computer vision),Minimum redundancy feature selection,Machine learning | Conference |
ISSN | ISBN | Citations |
2163-6966 | 978-1-4673-2744-2 | 0 |
PageRank | References | Authors |
0.34 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xue-qiang Zeng | 1 | 76 | 7.91 |
| Guo-Zheng Li | 2 | 368 | 42.62 |