Scalable random forests for massive data
文献类型：会议论文
| 作者 | Li bingguo; Chen xiaojun; Li Mark junjie; Huang Joshua zhexue; Feng shengzhong |
| 出版日期 | 2012 |
| 会议名称 | 16th Pacific-Asia Conference on Advances in Knowledge Discovery andData Mining, PAKDD 2012 |
| 会议地点 | Kuala Lumpur, Malaysia |
| 英文摘要 | This paper proposes a scalable random forest algorithm SRF with MapReduce implementation. A breadth-first approach is used to grow decision trees for arandom forest model. At each level of the trees, a pair of map and reduce functions split the nodes. A mapper is dispatched to a local machine to compute the local histograms of subspace features of the nodes from a data block. The local histograms are submitted to reducers to compute the global histograms from which the best split conditions of the nodes are calculated and sent to the controller on the master machine to update the random forest model. A random forest model is built with a sequence of map and reduce functions. Experiments on large synthetic data have shown that SRF is scalable to the number of trees and the number of examples. The SRF algorithm is able to build a random forest of 100 trees in a little more than 1 hour from 110 Gigabyte data with 1000 features and 10 million records. © 2012 Springer-Verlag.(18 refs) |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/4219]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 作者单位 | 2012 |
| 推荐引用方式 GB/T 7714 | Li bingguo,Chen xiaojun,Li Mark junjie,et al. Scalable random forests for massive data[C]. 见:16th Pacific-Asia Conference on Advances in Knowledge Discovery andData Mining, PAKDD 2012. Kuala Lumpur, Malaysia. |
入库方式： OAI收割
来源：深圳先进技术研究院
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