EuroVA2022
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Browsing EuroVA2022 by Author "b7736374-f807-4f21-82f0-e4a978fec7d3"
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Item EuroVa 2022: Frontmatter(The Eurographics Association, 2022) Bernard, Jürgen; Angelini, Marco; Bernard, Jürgen; Angelini, MarcoItem Interactive Visual Explanation of Incremental Data Labeling(The Eurographics Association, 2022) Beckmann, Raphael; Blaga, Cristian; El-Assady, Mennatallah; Zeppelzauer, Matthias; Bernard, Jürgen; Bernard, Jürgen; Angelini, MarcoWe present a visual analytics approach for the in-depth analysis and explanation of incremental machine learning processes that are based on data labeling. Our approach offers multiple perspectives to explain the process, i.e., data characteristics, label distribution, class characteristics, and classifier characteristics. Additionally, we introduce metrics from which we derive novel aggregated analytic views that enable the analysis of the process over time. We demonstrate the capabilities of our approach in a case study and thereby demonstrate how our approach improves the transparency of the iterative learning process.Item RankASco: A Visual Analytics Approach to Leverage Attribute-Based User Preferences for Item Rankings(The Eurographics Association, 2022) Schmid, Jenny; Cibulski, Lena; Hazwani, Ibrahim Al; Bernard, Jürgen; Bernard, Jürgen; Angelini, MarcoItem rankings are useful when a decision needs to be made, especially if there are multiple attributes to be considered. However, existing tools either do not support both categorical and numerical attributes, require programming expertise for expressing preferences on attributes, do not offer instant feedback, or lack flexibility in expressing various types of user preferences. In this work, we present RankASco: a human-centered visual analytics approach that supports the interactive and visual creation of rankings. RankASco leverages a series of visual interfaces, enabling broad user groups to a) select attributes of interest, b) express preferences on attribute scorings based on different mental models, and c) analyze and refine item ranking results.