Meta-Feature Engineering for Machine Learning-Based Automated Data Visualization 


Vol. 44,  No. 9, pp. 1788-1797, Sep.  2019
10.7840/kics.2019.44.9.1788


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  Abstract

This paper aims at realization of an automatic data visualization system based on machine learning, and introduces a metal-level feature engineering process to construct a visualization recommendation model. Basically, the visualization results can be varied according to the purpose of the data analysis, and as the understanding of the data becomes grow, more various results can be obtained. Through these experiments, we have designed various meta-feature variable 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 R datasets, UC Irvine ML Repository, and Data. world, and have found that the decision tree-based recommendation model provides the best performance.

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  Cite this article

[IEEE Style]

H. Choi and H. Kim, "Meta-Feature Engineering for Machine Learning-Based Automated Data Visualization," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 9, pp. 1788-1797, 2019. DOI: 10.7840/kics.2019.44.9.1788.

[ACM Style]

Hee-won Choi and Han-joon Kim. 2019. Meta-Feature Engineering for Machine Learning-Based Automated Data Visualization. The Journal of Korean Institute of Communications and Information Sciences, 44, 9, (2019), 1788-1797. DOI: 10.7840/kics.2019.44.9.1788.

[KICS Style]

Hee-won Choi and Han-joon Kim, "Meta-Feature Engineering for Machine Learning-Based Automated Data Visualization," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 9, pp. 1788-1797, 9. 2019. (https://doi.org/10.7840/kics.2019.44.9.1788)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880