Institutional Repository of Digital Factory Department
| A visualization recommendation approach based on machine learning | |
Wei SC(魏世超)1,2,3,4; Li X(李歆)1,2,3 ; Song PY(宋沛荫)2,3,4,5; Zhou XF(周晓锋)1,2,3; Zhang YC(张宜弛)1,2,3; Li S(李帅)1,2,3,4
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| Department | 数字工厂研究室 |
| Conference Name | 2020 10th International Workshop on Computer Science and Engineering, WCSE 2020 |
| Conference Date | June 19-21, 2020 |
| Conference Place | Shanghai, China |
| Author of Source | Bauman Moscow State Technical University ; Shanghai Maritime University ; Tokyo University of Science ; University of Houston-Downtown |
| Source Publication | WCSE 2020: 2020 10th International Workshop on Computer Science and Engineering |
| Publisher | International Workshop on Computer Science and Engineering (WCSE) |
| 2020 | |
| Pages | 6-12 |
| Indexed By | EI |
| EI Accession number | 20204209345249 |
| Contribution Rank | 1 |
| ISBN | 978-981-14-4787-7 |
| Keyword | visualization recommendation machine Learning classification model |
| Abstract | As an important means of data analysis, data visualization is used by more and more people. For most people who don't have visualization technology expertise, data visualization has some Visualization recommendation aims to lower the barrier to exploring basic visualizations by automatically generating results for analysts to search and select.This paper proposes a visual recommendation method based on machine learning, which can learn the most meaningful visualization results from many visualization practice datasets and mark them.Firstly, 22 data features and corresponding meaningful visualization types are extracted from 30 real visualization datasets. Then, binary classifiers are used to train the classification model, from which we can learn meaningful visualization and use crowdsourced testsets to test the accuracy.Finally, the results of multiple classifiers are fused to vote for multiple meaningful charts in the datasets.Experiments show that this method can effectively learn the meaningful visualization types in datasets, mark and recommend them to users. |
| Language | 英语 |
| Document Type | 会议论文 |
| Identifier | http://ir.sia.cn/handle/173321/27733 |
| Collection | 数字工厂研究室 |
| Corresponding Author | Zhou XF(周晓锋) |
| Affiliation | 1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 2.Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang 110016, China 3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China 4.University of Chinese Academy of Sciences, Beijing 100049, China 5.Shenyang University of Technology, China |
| Recommended Citation GB/T 7714 | Wei SC,Li X,Song PY,et al. A visualization recommendation approach based on machine learning[C]//Bauman Moscow State Technical University, Shanghai Maritime University, Tokyo University of Science, University of Houston-Downtown:International Workshop on Computer Science and Engineering (WCSE),2020:6-12. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| A visualization reco（49013KB） | 会议论文 | 开放获取 | CC BY-NC-SA | View Application Full Text | ||
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