Name
Abstract | ||
|---|---|---|
Boosting techniques have been applied to DNA microarray data because their high dimensionality has made them difficult to analyze. However, classification performance varies between boosting algorithms. We have investigated factors affecting error in boosting ensemble classifiers on DNA Microarray data: number of training samples, number of boosting iterations, complexity of base learners and diversity of models. Specifically we have applied diversity measures to investigate the relationships between model type, model accuracy, diversity and ensemble accuracy. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IJCNN.2010.5596600 | IJCNN |
Keywords | Field | DocType |
DNA,biology computing,data analysis,iterative methods,pattern classification,DNA microarray data,boosting algorithms,boosting ensemble classifiers,boosting ensemble performance,boosting iterations,boosting techniques,classification performance,diversity measures,ensemble accuracy,high dimensionality,model accuracy,model type | Dna microarray data,Pattern recognition,Computer science,Iterative method,Curse of dimensionality,Boosting (machine learning),Artificial intelligence,Machine learning | Conference |
ISSN | Citations | PageRank |
1098-7576 | 1 | 0.37 |
References | Authors | |
7 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Geoffrey R. Guile | 1 | 8 | 1.68 |
| Wenjia Wang | 2 | 57 | 9.12 |