Machine Learning-based Automated Data Visualization: A Meta-feature Engineering Approach

Hee Won Choi, Seung Yeop Shin, Han Joon Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper investigates an effective method to automatically visualize a given data set based on machine learning. Basically, the visualization results can be varied according to the purpose of the data analysis, and as the understanding of the data becomes larger, more various results can be obtained. This paper aims at realization of an automatic data visualization system based on machine learning, and introduces a meta-level feature engineering process to construct a visualization recommendation (classification) model. Through various experiments, we have designed various meta- feature variables to determine the significance of the visualization results in order to develop the automatic visualization system and constructed the visualization recommendation model using the meta-features. For performance evaluation, we have used three data sources including UCI ML Repository, Data.world, and R datasets, and have found that the decision tree-based recommendation model provides the best performance.

Original languageEnglish
Title of host publicationProceedings of the 2019 8th International Conference on Innovation, Communication and Engineering, ICICE 2019
EditorsShoou-Jinn Chang, Sheng-Joue Young, Artde Donald Kin-Tak Lam, Liang-Wen Ji, Hao-Ying Lu, Stephen D. Prior
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages107-109
Number of pages3
ISBN (Electronic)9781728158396
DOIs
StatePublished - Oct 2019
Event8th International Conference on Innovation, Communication and Engineering, ICICE 2019 - Zhengzhou, Henan Province, China
Duration: 25 Oct 201930 Oct 2019

Publication series

NameProceedings of the 2019 8th International Conference on Innovation, Communication and Engineering, ICICE 2019

Conference

Conference8th International Conference on Innovation, Communication and Engineering, ICICE 2019
Country/TerritoryChina
CityZhengzhou, Henan Province
Period25/10/1930/10/19

Keywords

  • data visualization
  • feature engineering
  • machine learning
  • metadata

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