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Title:
visualizing the random forest by 3d techniques
Author: Yang Min ; Xu Hexin ; Zhu Dingju ; Chen Huijuan
Source: Communications in Computer and Information Science
Conference Name: International Workshop on Internet of Things, IOT 2012
Conference Date: August 17, 2012 - August 29, 2012
Issued Date: 2012
Conference Place: Changsha, China
Keyword: Classification (of information) ; Data mining ; Decision making ; Decision trees ; Internet ; Three dimensional computer graphics ; Visualization
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
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.
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.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15948
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Yang Min,Xu Hexin,Zhu Dingju,et al. visualizing the random forest by 3d techniques[C]. 见:International Workshop on Internet of Things, IOT 2012. Changsha, China. August 17, 2012 - August 29, 2012.
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