Name
Abstract | ||
|---|---|---|
In this paper, ensembles of k-nearest neighbors classifiers are explored for gene expression cancer classification, where each classifier is linked to a randomly selected subset of genes. It is experimentally demonstrated using five datasets that such ensembles can yield both good accuracy and dimensionality reduction. If a characteristic called dataset complexity guides which random subset to include into an ensemble, then the ensemble achieves even better performance. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IJCNN.2008.4634077 | 2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8 |
Keywords | Field | DocType |
classification algorithms,gene expression,proteins,random variables,k nearest neighbor,dimensionality reduction,cancer,organisms,correlation,rna,amino acids,dna | k-nearest neighbors algorithm,Cancer classification,Random variable,Dimensionality reduction,Pattern recognition,Computer science,Noise level,Correlation,Artificial intelligence,Statistical classification,Classifier (linguistics),Machine learning | Conference |
ISSN | Citations | PageRank |
2161-4393 | 1 | 0.36 |
References | Authors | |
2 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oleg Okun | 1 | 308 | 28.56 |
| Helen Priisalu | 2 | 58 | 4.13 |