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  1. Article ; Online: Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023.

    Chatzimparmpas, Angelos / Kucher, Kostiantyn / Kerren, Andreas

    IEEE computer graphics and applications

    2024  Volume PP

    Abstract: Visualization for explainable and trustworthy machine learning remains one of the most important and heavily researched fields within information visualization and visual analytics with various application domains, such as medicine, finance, and ... ...

    Abstract Visualization for explainable and trustworthy machine learning remains one of the most important and heavily researched fields within information visualization and visual analytics with various application domains, such as medicine, finance, and bioinformatics. After our 2020 state-of-the-art report comprising 200 techniques, we have persistently collected peer-reviewed articles describing visualization techniques, categorized them based on the previously established categorization schema consisting of 119 categories, and provided the resulting collection of 542 techniques in an online survey browser. In this survey article, we present the updated findings of new analyses of this dataset as of fall 2023 and discuss trends, insights, and eight open challenges for using visualizations in machine learning. Our results corroborate the rapidly growing trend of visualization techniques for increasing trust in machine learning models in the past three years, with visualization found to help improve popular model explainability methods and check new deep learning architectures, for instance.
    Language English
    Publishing date 2024-01-31
    Publishing country United States
    Document type Journal Article
    ISSN 1558-1756
    ISSN (online) 1558-1756
    DOI 10.1109/MCG.2024.3360881
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: User Preferences of Spatio-Temporal Referencing Approaches For Immersive 3D Radar Charts

    Reski, Nico / Alissandrakis, Aris / Kerren, Andreas

    2023  

    Abstract: The use of head-mounted display technologies for virtual reality experiences is inherently single-user-centred, allowing for the visual immersion of its user in the computer-generated environment. This isolates them from their physical surroundings, ... ...

    Abstract The use of head-mounted display technologies for virtual reality experiences is inherently single-user-centred, allowing for the visual immersion of its user in the computer-generated environment. This isolates them from their physical surroundings, effectively preventing external visual information cues, such as the pointing and referral to an artifact by another user. However, such input is important and desired in collaborative scenarios when exploring and analyzing data in virtual environments together with a peer. In this article, we investigate different designs for making spatio-temporal references, i.e., visually highlighting virtual data artifacts, within the context of Collaborative Immersive Analytics. The ability to make references to data is foundational for collaboration, affecting aspects such as awareness, attention, and common ground. Based on three design options, we implemented a variety of approaches to make spatial and temporal references in an immersive virtual reality environment that featured abstract visualization of spatio-temporal data as 3D Radar Charts. We conducted a user study (n=12) to empirically evaluate aspects such as aesthetic appeal, legibility, and general user preference. The results indicate a unified favour for the presented location approach as a spatial reference while revealing trends towards a preference of mixed temporal reference approaches dependent on the task configuration: pointer for elementary, and outline for synoptic references. Based on immersive data visualization complexity as well as task reference configuration, we argue that it can be beneficial to explore multiple reference approaches as collaborative information cues, as opposed to following a rather uniform user interface design.

    Comment: 29 pages, 9 figures, 1 table
    Keywords Computer Science - Human-Computer Interaction ; H.5.2
    Subject code 004
    Publishing date 2023-03-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Designing a 3D Gestural Interface to Support User Interaction with Time-Oriented Data as Immersive 3D Radar Chart

    Reski, Nico / Alissandrakis, Aris / Kerren, Andreas

    2023  

    Abstract: The design of intuitive three-dimensional user interfaces is vital for interaction in virtual reality, allowing to effectively close the loop between a human user and the virtual environment. The utilization of 3D gestural input allows for useful hand ... ...

    Abstract The design of intuitive three-dimensional user interfaces is vital for interaction in virtual reality, allowing to effectively close the loop between a human user and the virtual environment. The utilization of 3D gestural input allows for useful hand interaction with virtual content by directly grasping visible objects, or through invisible gestural commands that are associated with corresponding features in the immersive 3D space. The design of such interfaces remains complex and challenging. In this article, we present a design approach for a three-dimensional user interface using 3D gestural input with the aim to facilitate user interaction within the context of Immersive Analytics. Based on a scenario of exploring time-oriented data in immersive virtual reality using 3D Radar Charts, we implemented a rich set of features that is closely aligned with relevant 3D interaction techniques, data analysis tasks, and aspects of hand posture comfort. We conducted an empirical evaluation (n=12), featuring a series of representative tasks to evaluate the developed user interface design prototype. The results, based on questionnaires, observations, and interviews, indicate good usability and an engaging user experience. We are able to reflect on the implemented hand-based grasping and gestural command techniques, identifying aspects for improvement in regard to hand detection and precision as well as emphasizing a prototype's ability to infer user intent for better prevention of unintentional gestures.

    Comment: 30 pages, 6 figures, 2 tables; this version corrects Figure 6 (boxplot of PU in the UES-SF scores) and related discussion in Section 6
    Keywords Computer Science - Human-Computer Interaction ; H.5.2
    Subject code 004 ; 005
    Publishing date 2023-03-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches.

    Chatzimparmpas, Angelos / Martins, Rafael M / Kucher, Kostiantyn / Kerren, Andreas

    IEEE transactions on visualization and computer graphics

    2022  Volume 28, Issue 4, Page(s) 1773–1791

    Abstract: The machine learning (ML) life cycle involves a series of iterative steps, from the effective gathering and preparation of the data-including complex feature engineering processes-to the presentation and improvement of results, with various algorithms to ...

    Abstract The machine learning (ML) life cycle involves a series of iterative steps, from the effective gathering and preparation of the data-including complex feature engineering processes-to the presentation and improvement of results, with various algorithms to choose from in every step. Feature engineering in particular can be very beneficial for ML, leading to numerous improvements such as boosting the predictive results, decreasing computational times, reducing excessive noise, and increasing the transparency behind the decisions taken during the training. Despite that, while several visual analytics tools exist to monitor and control the different stages of the ML life cycle (especially those related to data and algorithms), feature engineering support remains inadequate. In this paper, we present FeatureEnVi, a visual analytics system specifically designed to assist with the feature engineering process. Our proposed system helps users to choose the most important feature, to transform the original features into powerful alternatives, and to experiment with different feature generation combinations. Additionally, data space slicing allows users to explore the impact of features on both local and global scales. FeatureEnVi utilizes multiple automatic feature selection techniques; furthermore, it visually guides users with statistical evidence about the influence of each feature (or subsets of features). The final outcome is the extraction of heavily engineered features, evaluated by multiple validation metrics. The usefulness and applicability of FeatureEnVi are demonstrated with two use cases and a case study. We also report feedback from interviews with two ML experts and a visualization researcher who assessed the effectiveness of our system.
    MeSH term(s) Algorithms ; Computer Graphics ; Machine Learning
    Language English
    Publishing date 2022-02-25
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2022.3141040
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Information visualization courses for students with a computer science background.

    Kerren, Andreas

    IEEE computer graphics and applications

    2013  Volume 33, Issue 2, Page(s) 12–15

    Abstract: Linnaeus University offers two master's courses in information visualization for computer science students with programming experience. This article briefly describes the syllabi, exercises, and practices developed for these courses. ...

    Abstract Linnaeus University offers two master's courses in information visualization for computer science students with programming experience. This article briefly describes the syllabi, exercises, and practices developed for these courses.
    MeSH term(s) Computer Graphics ; Computers ; Curriculum ; Educational Measurement ; Humans ; Students
    Language English
    Publishing date 2013-03
    Publishing country United States
    Document type Journal Article
    ISSN 1558-1756
    ISSN (online) 1558-1756
    DOI 10.1109/MCG.2013.27
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Book ; Online: DeforestVis

    Chatzimparmpas, Angelos / Martins, Rafael M. / Telea, Alexandru C. / Kerren, Andreas

    Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps

    2023  

    Abstract: As the complexity of machine learning (ML) models increases and the applications in different (and critical) domains grow, there is a strong demand for more interpretable and trustworthy ML. One straightforward and model-agnostic way to interpret complex ...

    Abstract As the complexity of machine learning (ML) models increases and the applications in different (and critical) domains grow, there is a strong demand for more interpretable and trustworthy ML. One straightforward and model-agnostic way to interpret complex ML models is to train surrogate models, such as rule sets and decision trees, that sufficiently approximate the original ones while being simpler and easier-to-explain. Yet, rule sets can become very lengthy, with many if-else statements, and decision tree depth grows rapidly when accurately emulating complex ML models. In such cases, both approaches can fail to meet their core goal, providing users with model interpretability. We tackle this by proposing DeforestVis, a visual analytics tool that offers user-friendly summarization of the behavior of complex ML models by providing surrogate decision stumps (one-level decision trees) generated with the adaptive boosting (AdaBoost) technique. Our solution helps users to explore the complexity vs fidelity trade-off by incrementally generating more stumps, creating attribute-based explanations with weighted stumps to justify decision making, and analyzing the impact of rule overriding on training instance allocation between one or more stumps. An independent test set allows users to monitor the effectiveness of manual rule changes and form hypotheses based on case-by-case investigations. We show the applicability and usefulness of DeforestVis with two use cases and expert interviews with data analysts and model developers.

    Comment: This manuscript is currently under review
    Keywords Computer Science - Machine Learning ; Computer Science - Human-Computer Interaction
    Subject code 006
    Publishing date 2023-03-31
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  7. Book ; Online: MetaStackVis

    Ploshchik, Ilya / Chatzimparmpas, Angelos / Kerren, Andreas

    Visually-Assisted Performance Evaluation of Metamodels

    2022  

    Abstract: Stacking (or stacked generalization) is an ensemble learning method with one main distinctiveness from the rest: even though several base models are trained on the original data set, their predictions are further used as input data for one or more ... ...

    Abstract Stacking (or stacked generalization) is an ensemble learning method with one main distinctiveness from the rest: even though several base models are trained on the original data set, their predictions are further used as input data for one or more metamodels arranged in at least one extra layer. Composing a stack of models can produce high-performance outcomes, but it usually involves a trial-and-error process. Therefore, our previously developed visual analytics system, StackGenVis, was mainly designed to assist users in choosing a set of top-performing and diverse models by measuring their predictive performance. However, it only employs a single logistic regression metamodel. In this paper, we investigate the impact of alternative metamodels on the performance of stacking ensembles using a novel visualization tool, called MetaStackVis. Our interactive tool helps users to visually explore different singular and pairs of metamodels according to their predictive probabilities and multiple validation metrics, as well as their ability to predict specific problematic data instances. MetaStackVis was evaluated with a usage scenario based on a medical data set and via expert interviews.

    Comment: This manuscript is accepted for publication in Proceedings of the 16th IEEE Pacific Visualization Symposium (PacificVis '23)
    Keywords Computer Science - Machine Learning ; Computer Science - Human-Computer Interaction ; Statistics - Machine Learning
    Subject code 000 ; 006
    Publishing date 2022-12-07
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: HardVis

    Chatzimparmpas, Angelos / Paulovich, Fernando V. / Kerren, Andreas

    Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques

    2022  

    Abstract: Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real-world applications. Among a series of diverse techniques to solve this problem, sampling algorithms are regarded as an efficient ... ...

    Abstract Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real-world applications. Among a series of diverse techniques to solve this problem, sampling algorithms are regarded as an efficient solution. However, the problem is more fundamental, with many works emphasizing the importance of instance hardness. This issue refers to the significance of managing unsafe or potentially noisy instances that are more likely to be misclassified and serve as the root cause of poor classification performance. This paper introduces HardVis, a visual analytics system designed to handle instance hardness mainly in imbalanced classification scenarios. Our proposed system assists users in visually comparing different distributions of data types, selecting types of instances based on local characteristics that will later be affected by the active sampling method, and validating which suggestions from undersampling or oversampling techniques are beneficial for the ML model. Additionally, rather than uniformly undersampling/oversampling a specific class, we allow users to find and sample easy and difficult to classify training instances from all classes. Users can explore subsets of data from different perspectives to decide all those parameters, while HardVis keeps track of their steps and evaluates the model's predictive performance in a test set separately. The end result is a well-balanced data set that boosts the predictive power of the ML model. The efficacy and effectiveness of HardVis are demonstrated with a hypothetical usage scenario and a use case. Finally, we also look at how useful our system is based on feedback we received from ML experts.

    Comment: This manuscript is accepted for publication in Computer Graphics Forum (CGF)
    Keywords Computer Science - Machine Learning ; Computer Science - Human-Computer Interaction ; Statistics - Machine Learning
    Subject code 004
    Publishing date 2022-03-29
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections.

    Chatzimparmpas, Angelos / Martins, Rafael M / Kerren, Andreas

    IEEE transactions on visualization and computer graphics

    2020  Volume 26, Issue 8, Page(s) 2696–2714

    Abstract: t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with successful applications in a wide range of domains. Despite their usefulness, t-SNE projections can be hard to ... ...

    Abstract t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with successful applications in a wide range of domains. Despite their usefulness, t-SNE projections can be hard to interpret or even misleading, which hurts the trustworthiness of the results. Understanding the details of t-SNE itself and the reasons behind specific patterns in its output may be a daunting task, especially for non-experts in dimensionality reduction. In this article, we present t-viSNE, an interactive tool for the visual exploration of t-SNE projections that enables analysts to inspect different aspects of their accuracy and meaning, such as the effects of hyper-parameters, distance and neighborhood preservation, densities and costs of specific neighborhoods, and the correlations between dimensions and visual patterns. We propose a coherent, accessible, and well-integrated collection of different views for the visualization of t-SNE projections. The applicability and usability of t-viSNE are demonstrated through hypothetical usage scenarios with real data sets. Finally, we present the results of a user study where the tool's effectiveness was evaluated. By bringing to light information that would normally be lost after running t-SNE, we hope to support analysts in using t-SNE and making its results better understandable.
    Language English
    Publishing date 2020-04-13
    Publishing country United States
    Document type Journal Article
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2020.2986996
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics.

    Chatzimparmpas, Angelos / Martins, Rafael M / Kucher, Kostiantyn / Kerren, Andreas

    IEEE transactions on visualization and computer graphics

    2021  Volume 27, Issue 2, Page(s) 1547–1557

    Abstract: In machine learning (ML), ensemble methods-such as bagging, boosting, and stacking-are widely-established approaches that regularly achieve top-notch predictive performance. Stacking (also called "stacked generalization") is an ensemble method that ... ...

    Abstract In machine learning (ML), ensemble methods-such as bagging, boosting, and stacking-are widely-established approaches that regularly achieve top-notch predictive performance. Stacking (also called "stacked generalization") is an ensemble method that combines heterogeneous base models, arranged in at least one layer, and then employs another metamodel to summarize the predictions of those models. Although it may be a highly-effective approach for increasing the predictive performance of ML, generating a stack of models from scratch can be a cumbersome trial-and-error process. This challenge stems from the enormous space of available solutions, with different sets of data instances and features that could be used for training, several algorithms to choose from, and instantiations of these algorithms using diverse parameters (i.e., models) that perform differently according to various metrics. In this work, we present a knowledge generation model, which supports ensemble learning with the use of visualization, and a visual analytics system for stacked generalization. Our system, StackGenVis, assists users in dynamically adapting performance metrics, managing data instances, selecting the most important features for a given data set, choosing a set of top-performant and diverse algorithms, and measuring the predictive performance. In consequence, our proposed tool helps users to decide between distinct models and to reduce the complexity of the resulting stack by removing overpromising and underperforming models. The applicability and effectiveness of StackGenVis are demonstrated with two use cases: a real-world healthcare data set and a collection of data related to sentiment/stance detection in texts. Finally, the tool has been evaluated through interviews with three ML experts.
    Language English
    Publishing date 2021-01-28
    Publishing country United States
    Document type Journal Article
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2020.3030352
    Database MEDical Literature Analysis and Retrieval System OnLINE

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