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  • 1.
    Chatzimparmpas, Angelos
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Martins, Rafael Messias
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Jusufi, Ilir
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Kucher, Kostiantyn
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV).
    Rossi, Fabrice
    Université Paris Dauphine, PSL University, France.
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations2020Inngår i: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 39, nr 3, s. 713-756Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Machine learning (ML) models are nowadays used in complex applications in various domains such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web-based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.

  • 2.
    Kruiger, Johannes F.
    et al.
    University of Groningen, Netherlands.
    Rauber, Paulo E.
    University of Groningen, Netherlands.
    Martins, Rafael Messias
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV).
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV).
    Kobourov, Stephen
    The University of Arizona, USA.
    Telea, Alexandru C.
    University of Groningen, Netherlands.
    Graph Layouts by t-SNE2017Inngår i: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 36, nr 3, s. 283-294Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We propose a new graph layout method based on a modification of the t-distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction technique. Although t-SNE is one of the best techniques for visualizing high-dimensional data as 2D scatterplots, t-SNE has not been used in the context of classical graph layout. We propose a new graph layout method, tsNET, based on representing a graph with a distance matrix, which together with a modified t-SNE cost function results in desirable layouts. We evaluate our method by a formal comparison with state-of-the-art methods, both visually and via established quality metrics on a comprehensive benchmark, containing real-world and synthetic graphs. As evidenced by the quality metrics and visual inspection, tsNET produces excellent layouts.

  • 3.
    Kucher, Kostiantyn
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV).
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV).
    The State of the Art in Sentiment Visualization2018Inngår i: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 37, nr 1, s. 71-96, artikkel-id CGF13217Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Visualization of sentiments and opinions extracted from or annotated in texts has become a prominent topic of research over the last decade. From basic pie and bar charts used to illustrate customer reviews to extensive visual analytics systems involving novel representations, sentiment visualization techniques have evolved to deal with complex multidimensional data sets, including temporal, relational, and geospatial aspects. This contribution presents a survey of sentiment visualization techniques based on a detailed categorization. We describe the background of sentiment analysis, introduce a categorization for sentiment visualization techniques that includes 7 groups with 35 categories in total, and discuss 132 techniques from peer-reviewed publications together with an interactive web-based survey browser. Finally, we discuss insights and opportunities for further research in sentiment visualization. We expect this survey to be useful for visualization researchers whose interests include sentiment or other aspects of text data as well as researchers and practitioners from other disciplines in search of efficient visualization techniques applicable to their tasks and data. 

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