Name
Abstract | ||
|---|---|---|
This study attempts to propose an improved decision forest (IDF) with an integrated graphical user interface. Based on four gene expression data sets, the IDF not only outperforms the original decision forest, but also is superior or comparable to other state-of-the-art machine learning methods, especially in dealing with high dimensional data. With an integrated built-in feature selection (FS) mechanism and fewer parameters to tune, it can be trained more efficiently than methods such as support vector machine, and can be built with much fewer trees than other popular tree-based ensemble methods. Moreover, it suffers less from the curse of dimensionality. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.compbiomed.2010.06.004 | Comp. in Bio. and Med. |
Keywords | DocType | Volume |
fewer parameter,support vector machine,state-of-the-art machine,integrated graphical user interface,high dimensional data,gene expression data set,improved decision forest,integrated built-in feature selection,fewer tree,original decision forest | Journal | 40 |
Issue | ISSN | Citations |
8 | 1879-0534 | 11 |
PageRank | References | Authors |
0.60 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianping Huang | 1 | 30 | 5.34 |
| Hong Fang | 2 | 523 | 31.42 |
| Xiaohui Fan | 3 | 61 | 5.70 |