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| visualizing the random forest by 3d techniques | |
| Yang Min; Xu Hexin; Zhu Dingju; Chen Huijuan | |
| 2012 | |
| Conference Name | International Workshop on Internet of Things, IOT 2012 |
| Source | Communications in Computer and Information Science |
| Pages | 639-645 |
| Conference Date | August 17, 2012 - August 29, 2012 |
| Conference Place | Changsha, China |
| Indexed Type | EI |
| ISSN | 1865-0929 |
| ISBN | 9783642324260 |
| Department | (1) Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China; (2) School of Software Sichuan University China |
| English Abstract | Random forest which contains a set of decision trees is a popular method in data mining. It has the advantages of high accuracy, high learning speed and the ability of dealing with high dimensional data. The decision model from the training process, however, is non-deterministic because of the sampling process. Although we can calculate correlations between different decision trees to infer the performance, it's not comprehensive to non-specialists. So, the goal of this project is to find a way of visualizing the learning process and the final model using 3D techniques. As a consequence, it can help in model selection by visualizing the patterns of different trees in terms of density, similarity and so on. Moreover, it can help users to understand how rules are learnt and then applied in decision making. Finally, it can provide an interactive interface for manual modifications (e.g. pruning). © 2012 Springer-Verlag.; Random forest which contains a set of decision trees is a popular method in data mining. It has the advantages of high accuracy, high learning speed and the ability of dealing with high dimensional data. The decision model from the training process, however, is non-deterministic because of the sampling process. Although we can calculate correlations between different decision trees to infer the performance, it's not comprehensive to non-specialists. So, the goal of this project is to find a way of visualizing the learning process and the final model using 3D techniques. As a consequence, it can help in model selection by visualizing the patterns of different trees in terms of density, similarity and so on. Moreover, it can help users to understand how rules are learnt and then applied in decision making. Finally, it can provide an interactive interface for manual modifications (e.g. pruning). © 2012 Springer-Verlag. |
| Keyword | Classification (Of Information) Data Mining Decision Making Decision Trees Internet Three Dimensional Computer Graphics Visualization |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15948 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Yang Min,Xu Hexin,Zhu Dingju,et al. visualizing the random forest by 3d techniques[C],2012:639-645. |
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