Computational Visual Media

, Volume 3, Issue 2, pp 161–175 | Cite as

EasySVM: A visual analysis approach for open-box support vector machines

  • Yuxin Ma
  • Wei Chen
  • Xiaohong Ma
  • Jiayi Xu
  • Xinxin Huang
  • Ross Maciejewski
  • Anthony K. H. Tung
Open Access
Research Article


Support vector machines (SVMs) are supervised learning models traditionally employed for classification and regression analysis. In classification analysis, a set of training data is chosen, and each instance in the training data is assigned a categorical class. An SVM then constructs a model based on a separating plane that maximizes the margin between different classes. Despite being one of the most popular classification models because of its strong performance empirically, understanding the knowledge captured in an SVM remains difficult. SVMs are typically applied in a black-box manner where the details of parameter tuning, training, and even the final constructed model are hidden from the users. This is natural since these details are often complex and difficult to understand without proper visualization tools. However, such an approach often brings about various problems including trial-and-error tuning and suspicious users who are forced to trust these models blindly.

The contribution of this paper is a visual analysis approach for building SVMs in an open-box manner. Our goal is to improve an analyst’s understanding of the SVM modeling process through a suite of visualization techniques that allow users to have full interactive visual control over the entire SVM training process. Our visual exploration tools have been developed to enable intuitive parameter tuning, training data manipulation, and rule extraction as part of the SVM training process. To demonstrate the efficacy of our approach, we conduct a case study using a real-world robot control dataset.


support vector machines (SVMs) rule extraction visual classification high-dimensional visualization visual analysis 



This work was supported in part by the National Basic Research Program of China (973 Program, No. 2015CB352503), the Major Program of National Natural Science Foundation of China (No. 61232012), and the National Natural Science Foundation of China (No. 61422211).

Supplementary material

41095_2017_77_MOESM1_ESM.mp4 (12.4 mb)
Supplementary material, approximately 12.3 MB.


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Copyright information

© The Author(s) 2017

Open Access The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Yuxin Ma
    • 1
  • Wei Chen
    • 1
  • Xiaohong Ma
    • 1
  • Jiayi Xu
    • 1
  • Xinxin Huang
    • 1
  • Ross Maciejewski
    • 2
  • Anthony K. H. Tung
    • 3
  1. 1.State Key Lab of CAD&CGZhejiang UniversityHangzhouChina
  2. 2.Arizona State UniversityTempeUSA
  3. 3.National University of SingaporeSingaporeSingapore

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