图 书 馆
斜向随机森林On oblique random forests |
|
| 课程网址: | https://videolectures.net/videos/ecmlpkdd2011_menze_forests |
| 主讲教师: | Bjoern H. Menze |
| 开课单位: | 2011会议 |
| 开课时间: | 2011-11-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In his original paper on random forests, Breiman proposed two different decision tree ensembles: one generated from "orthogonal" trees with thresholds on individual features in every split, and one from "oblique" trees separating the feature space by randomly oriented hyperplanes. In spite of a rising interest in the random forest framework, however, ensembles built from orthogonal trees (RF) have gained most, if not all, attention so far. In the present work we propose to employ "oblique" random forests (oRF) built from multivariate trees which explicitly learn optimal split directions at internal nodes using linear discriminative models, rather than using random coefficients as the original oRF. This oRF outperforms RF, as well as other classifiers, on nearly all data sets but those with discrete factorial features. Learned node models perform distinctively better than random splits. An oRF feature importance score shows to be preferable over standard RF feature importance scores such as Gini or permutation importance. The topology of the oRF decision space appears to be smoother and better adapted to the data, resulting in improved generalization performance. Overall, the oRF propose here may be preferred over standard RF on most learning tasks involving numerical and spectral data. |
| 关 键 词: | 斜向随机森林; 原始论文; 决策树集成 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2024-10-22：liyq |
| 最后编审: | 2024-10-22：liyq |
| 阅读次数: | 6 |