ScagCNN: Estimating Visual Characterizations of 2D Scatterplots via Convolution Neural Network

Vung Pham, Ngan V.T. Nguyen, Tommy Dang

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

2 Scopus citations

Abstract

Scagnostics is a set of visual features that characterizes the data distribution of a 2D scatterplot and has been used in a wide range of applications. However, calculating the scagnostics scores involves computationally expensive algorithms. Moreover, the algorithms are sensitive to the slight changes in the underlying data distribution within the scatterplot. Therefore, this work provides a machine learning model, called ScagCNN, to estimate the scagnostics scores. This model aims to improve the scagnostics computation time and to reduce the sensitivity to the small shifts in the data distribution. This work also provides a web prototype to explore the predictive performance of the model and to give a visual explanation about whether a prediction is accurate. Furthermore, we test the performance of our solution on datasets of various sizes.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Advances in Information Technology, IAIT 2020
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450377591
DOIs
StatePublished - Jul 1 2020
Event11th International Conference on Advances in Information Technology, IAIT 2020 - Bangkok, Thailand
Duration: Jul 1 2020Jul 3 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference11th International Conference on Advances in Information Technology, IAIT 2020
Country/TerritoryThailand
CityBangkok
Period07/1/2007/3/20

Keywords

  • Convolution Neural Network
  • Scagnostics
  • Visual features

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