TY - GEN
T1 - ScagCNN
T2 - 11th International Conference on Advances in Information Technology, IAIT 2020
AU - Pham, Vung
AU - Nguyen, Ngan V.T.
AU - Dang, Tommy
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/7/1
Y1 - 2020/7/1
N2 - 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.
AB - 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.
KW - Convolution Neural Network
KW - Scagnostics
KW - Visual features
UR - http://www.scopus.com/inward/record.url?scp=85123041711&partnerID=8YFLogxK
U2 - 10.1145/3406601.3406644
DO - 10.1145/3406601.3406644
M3 - Conference contribution
AN - SCOPUS:85123041711
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 11th International Conference on Advances in Information Technology, IAIT 2020
PB - Association for Computing Machinery
Y2 - 1 July 2020 through 3 July 2020
ER -