Name
Abstract | ||
|---|---|---|
We have investigated strategies for enhancing ensemble learning algorithms for the analysis of high-dimensional biological data. Specifically we investigated strategies to force classifiers to consider the possible interactions between features. As a result an algorithm that induces decision trees with a feature non-replacement mechanism has been devised and tested on DNA microarray and proteomic datasets. The results show that feature non-replacement enables decision trees deeper than simple stumps to be used, thereby allowing feature interaction to be taken into account. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IJCNN.2008.4634111 | 2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8 |
Keywords | Field | DocType |
artificial neural networks,dna microarray,dna,ensemble learning,boosting,decision tree,learning artificial intelligence,biological data,decision trees,neural networks,proteins | Decision tree,Biological data,Pattern recognition,Computer science,Boosting (machine learning),Artificial intelligence,Artificial neural network,Ensemble learning,DNA microarray,Machine learning | Conference |
ISSN | Citations | PageRank |
2161-4393 | 3 | 0.44 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Geoffrey R. Guile | 1 | 8 | 1.68 |
| Wenjia Wang | 2 | 57 | 9.12 |