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A Visual Analytics System for Interpretable Machine Learning
http://doi.org/10.5626/JOK.2023.50.1.57
Interpretable machine learning is a technology that assists people understand the behavior and prediction of machine learning systems. This study proposes a visual analytics system that can interpret the relationship between how machine learning models relate output results from input data. It supports users to interpret machine learning models easily and clearly. The visual analytics system proposed in this study takes an approach to effectively interpret the machine learning model through an iterative adjustment procedure that filters and groups model decision results according to input variables, target variables, and predicted/classified values. Through use case analysis and in-depth user interviews, we confirmed that our system could provide insights into the complex behavior of machine learning models, gain scientific understanding of input variables, target variables, and model predictions, and help users understand the stability and reliability of models.
Spatiotemporal Data Visualization using Gravity Model
Seokyeon Kim, Hanbyul Yeon, Yun Jang
Visual analysis of spatiotemporal data has focused on a variety of techniques for analyzing and exploring the data. The goal of these techniques is to explore the spatiotemporal data using time information, discover patterns in the data, and analyze spatiotemporal data. The overall trend flow patterns help users analyze geo-referenced temporal events. However, it is difficult to extract and visualize overall trend flow patterns using data that has no trajectory information for movements. In order to visualize overall trend flow patterns, in this paper, we estimate continuous distributions of discrete events over time using KDE, and we extract vector fields from the continuous distributions using the gravity model. We then apply our technique on twitter data to validate techniques.
Visual Analytics for Abnormal Event detection using Seasonal-Trend Decomposition and Serial-Correlation
In this paper, we present a visual analytics system that uses serial- correlation to detect an abnormal event in spatio-temporal data. Our approach extracts the topic-model from spatio-temporal tweets and then filters the abnormal event candidates using a seasonal-trend decomposition procedure based on Loess smoothing (STL). We re-extract the topic from the candidates, and then, we apply STL to the second candidate. Finally, we analyze the serial- correlation between the first candidates and the second candidate in order to detect abnormal events. We have used a visual analytic approach to detect the abnormal events, and therefore, the users can intuitively analyze abnormal event trends and cyclical patterns. For the case study, we have verified our visual analytics system by analyzing information related to two different events: the ‘Gyeongju Mauna Resort collapse’ and the ‘Jindo-ferry sinking’.
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