Beckmann, RaphaelBlaga, CristianEl-Assady, MennatallahZeppelzauer, MatthiasBernard, JürgenBernard, JürgenAngelini, Marco2022-06-022022-06-022022978-3-03868-183-02664-4487https://doi.org/10.2312/eurova.20221073https://diglib.eg.org:443/handle/10.2312/eurova20221073We 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.Attribution 4.0 International License10.2312/eurova.2022107313-175 pages