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  • 1.
    Chatzimparmpas, Angelos
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Visual Analytics for Explainable and Trustworthy Machine Learning2023Doktorsavhandling, monografi (Övrigt vetenskapligt)
    Abstract [sv]

    Användningen av artificiell intelligens och maskininlärning har exploderat i popularitet de senaste åren, med många olika typer av modeller för att tolka och förutse mönster och trender i data från olika områden. Ju mer komplexa dessa modeller blir, desto vanligare är det att de behandlas som “svarta lådor” vilka inte medger någon insyn i hur ett visst utfall har beräknats. Detta gör det svårt för användare att utvärdera och lita på resultaten, vilket i sin tur försvårar användning i situationer där beslut av vikt ska fattas. Även om automatiserade metoder delvis kan hantera denna problematik, tyder de senaste forskningsresultaten på att dessa också bör kombineras med innovativa metoder inom informationsvisualisering och visuell analys för att ge bästa effekt. Denna kombination kan ge fördjupade insikter som kan användas för att förbättra modellernas förmåga samt för att öka tillförlitligheten i, och förtroendet för, den övergripande processen. Inom forskningsområdet visuell analys kombineras statistiska modeller och maskininlärning med interaktiva visuella gränssnitt, vilket möjliggör för domänexperter att analysera stora och komplexa datamängder, samt ger dem möjlighet att använda sina expertkunskaper för att utveckla och förbättra de underliggande modellerna.

    De två huvudmålen för denna avhandling är att: (1) fokusera på metodologiska aspekter genom kvalitativa och kvantitativa metaanalyser i syfte att hjälpa forskare inom området att överblicka existerande litteratur och i syfte att lyfta fram kvarvarande utmaningar, samt (2) fokusera på tekniska lösningar genom att utveckla visuella analysmetoder för olika maskininlärningsmodeller, såsom dimensionsreducering och ensembleinlärning. För att uppnå det första målet definierar, kategoriserar och detaljgranskar vi former för visuell representation av tillförlitlighet i existerande maskininlärningsramverk, och utifrån detta formulerar vi riktlinjer för design av nya visualiseringar inom området. För att uppnå det andra målet diskuterar vi flera av våra egenutvecklade visuella analysverktyg och system, som utvecklats i syfte att möjliggöra specifik forskning på de olika stegen i ett generellt maskininlärningsramverk (vilket typiskt består av: databehandling, dataförädling, inställning av parametrar, förståelse, felsökning, förbättring, samt jämförelse av olika modeller). Våra metoder kan appliceras på många olika typer av data, men riktar sig främst mot data i tabellformat från områden såsom hälsovård och finans. Tillämplighet och relevans har validerats med hjälp av fallstudier, användningsfall, intervjuer med experter, användarstudier och diskussioner rörande begränsningar och möjliga alternativa designlösningar. Innehållet i denna avhandling öppnar upp nya inriktningar för forskning i visuell analys inom förklarlig och pålitlig maskininlärning.

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  • 2.
    Chatzimparmpas, Angelos
    et al.
    University of Western Macedonia, Greece.
    Bibi, Stamatia
    University of Western Macedonia, Greece.
    Maintenance process modeling and dynamic estimations based on Bayesian networks and association rules2019Ingår i: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 31, nr 9, s. 1-25, artikel-id e2163Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Managing the maintenance process and estimating accurately the effort and duration required for a new release is considered to be a crucial task as it affects successful software project survival and progress over time. In this study, we propose the combination of two well-known machine learning (ML) techniques, Bayesian Networks (BNs), and Association Rules (ARs) for modeling the maintenance process by identifying the relationships among the internal and external quality metrics related to a particular project release to both the maintainability of the project and the maintenance process indicators (i.e., effort and duration). We also exploit Bayesian inference, to test the effect of certain changes in internal and external project factors to the maintainability of a project. We evaluate our approach through a case study on 957 releases of five open source JavaScript applications. The results show that the maintainability of a release, the changes observed between subsequent releases, and the time required between two releases can be accurately predicted from size, complexity, and activity metrics. The proposed combined approach achieves higher accuracy when evaluated against the BN model accuracy.

  • 3.
    Chatzimparmpas, Angelos
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Bibi, Stamatia
    University of Western Macedonia, Greece.
    Zozas, Ioannis
    University of Western Macedonia, Greece.
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Analyzing the Evolution of JavaScript Applications2019Ingår i: Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE / [ed] Damiani, E; Spanoudakis, G; Maciaszek, L, SciTePress, 2019, Vol. 1, s. 359-366Konferensbidrag (Refereegranskat)
    Abstract [en]

    Software evolution analysis can shed light on various aspects of software development and maintenance. Up to date, there is little empirical evidence on the evolution of JavaScript (JS) applications in terms of maintainability and changeability, even though JavaScript is among the most popular scripting languages for front-end web applications. In this study, we investigate JS applications’ quality and changeability trends over time by examining the relevant Laws of Lehman. We analyzed over 7,500 releases of JS applications and reached some interesting conclusions. The results show that JS applications continuously change and grow, there are no clear signs of quality degradation while the complexity remains the same over time, despite the fact that the understandability of the code deteriorates.

  • 4.
    Chatzimparmpas, Angelos
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). Northwestern University, USA.
    Kucher, Kostiantyn
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). Linköping University, Sweden.
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). Linköping University, Sweden.
    Visualization for Trust in Machine Learning Revisited: The State of the Field in 20232024Ingår i: IEEE Computer Graphics and Applications, ISSN 0272-1716, E-ISSN 1558-1756, Vol. 44, nr 3, s. 99-113Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    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.

  • 5.
    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).
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    A survey of surveys on the use of visualization for interpreting machine learning models2020Ingår i: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 19, nr 3, s. 207-233Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Research in machine learning has become very popular in recent years, with many types of models proposed to comprehend and predict patterns and trends in data originating from different domains. As these models get more and more complex, it also becomes harder for users to assess and trust their results, since their internal operations are mostly hidden in black boxes. The interpretation of machine learning models is currently a hot topic in the information visualization community, with results showing that insights from machine learning models can lead to better predictions and improve the trustworthiness of the results. Due to this, multiple (and extensive) survey articles have been published recently trying to summarize the high number of original research papers published on the topic. But there is not always a clear definition of what these surveys cover, what is the overlap between them, which types of machine learning models they deal with, or what exactly is the scenario that the readers will find in each of them. In this article, we present a metaanalysis (i.e. a ‘‘survey of surveys’’) of manually collected survey papers that refer to the visual interpretation of machine learning models, including the papers discussed in the selected surveys. The aim of our article is to serve both as a detailed summary and as a guide through this survey ecosystem by acquiring, cataloging, and presenting fundamental knowledge of the state of the art and research opportunities in the area. Our results confirm the increasing trend of interpreting machine learning with visualizations in the past years, and that visualization can assist in, for example, online training processes of deep learning models and enhancing trust into machine learning. However, the question of exactly how this assistance should take place is still considered as an open challenge of the visualization community.

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  • 6.
    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).
    Rossi, Fabrice
    Université Paris Dauphine, 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 Visualizations2020Ingår i: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 39, nr 3, s. 713-756Artikel i tidskrift (Refereegranskat)
    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.

  • 7.
    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).
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    t-viSNE: A Visual Inspector for the Exploration of t-SNE2018Ingår i: Presented at IEEE Information Visualization  (VIS '18), Berlin, Germany, 21-26 October, 2018, 2018Konferensbidrag (Refereegranskat)
    Abstract [en]

    The use of t-Distributed Stochastic Neighborhood Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with applications published in a wide range of domains. Despite their usefulness, t-SNE plots can sometimes be hard to interpret or even misleading, which hurts the trustworthiness of the results. By opening the black box of the algorithm and showing insights into its behavior through visualization, we may learn how to use it in a more effective way. In this work, we present t-viSNE, a visual inspection tool that enables users to explore anomalies and assess the quality of t-SNE results by bringing forward aspects of the algorithm that would normally be lost after the dimensionality reduction process is finished.

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  • 8.
    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).
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections2020Ingår i: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 26, nr 8, s. 2696-2714Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    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 work, 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.

  • 9.
    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).
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). Linköping University, Sweden.
    VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees2023Ingår i: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 22, nr 2, s. 115-139Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Bagging and boosting are two popular ensemble methods in machine learning (ML) that produce many individual decision trees. Due to the inherent ensemble characteristic of these methods, they typically outperform single decision trees or other ML models in predictive performance. However, numerous decision paths are generated for each decision tree, increasing the overall complexity of the model and hindering its use in domains that require trustworthy and explainable decisions, such as finance, social care, and health care. Thus, the interpretability of bagging and boosting algorithms—such as random forest and adaptive boosting—reduces as the number of decisions rises. In this paper, we propose a visual analytics tool that aims to assist users in extracting decisions from such ML models via a thorough visual inspection workflow that includes selecting a set of robust and diverse models (originating from different ensemble learning algorithms), choosing important features according to their global contribution, and deciding which decisions are essential for global explanation (or locally, for specific cases). The outcome is a final decision based on the class agreement of several models and the explored manual decisions exported by users. We evaluated the applicability and effectiveness of VisRuler via a use case, a usage scenario, and a user study. The evaluation revealed that most users managed to successfully use our system to explore decision rules visually, performing the proposed tasks and answering the given questions in a satisfying way.

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  • 10.
    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).
    Kucher, Kostiantyn
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). Linköping University, Sweden.
    Empirical Study: Visual Analytics for Comparing Stacking to Blending Ensemble Learning2021Ingår i: Proceedings of the 23rd International Conference on Control Systems and Computer Science (CSCS23), 26–28 May 2021, Bucharest, Romania / [ed] Ioan Dumitrache, Adina Magda Florea, Mihnea-Alexandru Moisescu, Florin Pop, and Alexandru Dumitraşcu, IEEE, 2021, s. 1-8Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    Stacked generalization (also called stacking) is an ensemble method in machine learning that uses a metamodel to combine the predictive results of heterogeneous base models arranged in at least one layer. K-fold cross-validation is employed at the various stages of training in this method. Nonetheless, another validation strategy is to try out several splits of data leading to different train and test sets for the base models and then use only the latter to train the metamodel—this is known as blending. In this work, we present a modification of an existing visual analytics system, entitled StackGenVis, that now supports the process of composing robust and diverse ensembles of models with both aforementioned methods. We have built multiple ensembles using our system with the two respective methods, and we tested the performance with six small- to large-sized data sets. The results indicate that stacking is significantly more powerful than blending based on three performance metrics. However, the training times of the base models and the final ensembles are lower and more stable during various train/test splits in blending rather than stacking.

  • 11.
    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).
    Kucher, Kostiantyn
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). Linköping University, Sweden.
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). Linköping University, Sweden.
    FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches2022Ingår i: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 28, nr 4, s. 1773-1791Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    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.

  • 12.
    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).
    Kucher, Kostiantyn
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics2021Ingår i: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 27, nr 2, s. 1547-1557Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    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.

  • 13.
    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).
    Kucher, Kostiantyn
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). Linköping University, Sweden.
    VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization2021Ingår i: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 40, nr 3, s. 201-214Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    During the training phase of machine learning (ML) models, it is usually necessary to configure several hyperparameters. This process is computationally intensive and requires an extensive search to infer the best hyperparameter set for the given problem. The challenge is exacerbated by the fact that most ML models are complex internally, and training involves trial-and-error processes that could remarkably affect the predictive result. Moreover, each hyperparameter of an ML algorithm is potentially intertwined with the others, and changing it might result in unforeseeable impacts on the remaining hyperparameters. Evolutionary optimization is a promising method to try and address those issues. According to this method, performant models are stored, while the remainder are improved through crossover and mutation processes inspired by genetic algorithms. We present VisEvol, a visual analytics tool that supports interactive exploration of hyperparameters and intervention in this evolutionary procedure. In summary, our proposed tool helps the user to generate new models through evolution and eventually explore powerful hyperparameter combinations in diverse regions of the extensive hyperparameter space. The outcome is a voting ensemble (with equal rights) that boosts the final predictive performance. The utility and applicability of VisEvol are demonstrated with two use cases and interviews with ML experts who evaluated the effectiveness of the tool.

  • 14.
    Chatzimparmpas, Angelos
    et al.
    Northwestern University, USA.
    Martins, Rafael Messias
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Telea, Alexandru C.
    Utrecht University, Netherlands.
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). Linköping University, Sweden.
    DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps2024Ingår i: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    As the complexity of Machine Learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and trustworthy ML. A direct, model-agnostic, way to interpret such 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. To tackle this, we propose DeforestVis, a visual analytics tool that offers summarization of the behavior of complex ML models by providing surrogate decision stumps (one-level decision trees) generated with the Adaptive Boosting (AdaBoost) technique. DeforestVis 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 analyses. We show the applicability and usefulness of DeforestVis with two use cases and expert interviews with data analysts and model developers.

  • 15.
    Chatzimparmpas, Angelos
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Park, Vilhelm
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). Linköping University, Sweden.
    Evaluating StackGenVis with a Comparative User Study2022Ingår i: Proceedings of the 15th IEEE Pacific Visualization Symposium (PacificVis '22), IEEE, 2022, s. 161-165Konferensbidrag (Refereegranskat)
    Abstract [en]

    Stacked generalization (also called stacking) is an ensemble method in machine learning that deploys a metamodel to summarize the predictive results of heterogeneous base models organized into one or more layers. Despite being capable of producing high-performance results, building a stack of models can be a trial-and-error procedure. Thus, our previously developed visual analytics system, entitled StackGenVis, was designed to monitor and control the entire stacking process visually. In this work, we present the results of a comparative user study we performed for evaluating the StackGenVis system. We divided the study participants into two groups to test the usability and effectiveness of StackGenVis compared to Orange Visual Stacking (OVS) in an exploratory usage scenario using healthcare data. The results indicate that StackGenVis is significantly more powerful than OVS based on the qualitative feedback provided by the participants. However, the average completion time for all tasks was comparable between both tools.

  • 16.
    Chatzimparmpas, Angelos
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Paulovich, Fernando V.
    Eindhoven University of Technology, Netherlands.
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). Linköping University, Sweden.
    HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques2023Ingår i: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 42, nr 1, s. 135-154Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    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.

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  • 17.
    Kokkonis, George
    et al.
    Western Macedonia University of Applied Sciences, Greece.
    Chatzimparmpas, Angelos
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Kontogiannis, Sotirios
    University of Ioannina, Greece.
    Middleware IoT protocols performance evaluation for carrying out clustered data2018Ingår i: 2018 South-Eastern European Design Automation, Computer Engineering, Computer Networks and Society Media Conference (SEEDA_CECNSM), IEEE, 2018Konferensbidrag (Refereegranskat)
    Abstract [en]

    Several IoT middleware protocols have been proposed for the wireless IoT data transfer. The main representatives are the Constrained Application Protocol(CoAP), the Simple Object Access Protocol (SOAP), the message queuing Telemetry Transport (MQTT) and the HypertextTransfer Protocol (HTTP). Protocols deployment constraints are the message delay, message loss, processing effort and power consumption that IoT devices demand, in order to successfully transfer wireless data. In exchange for the reduction of device energy consumption, many of these IoT protocols try to lower the data throughput, minimize security, or even limit coverage. In this paper authors compare the performance of IoT application protocols using Machine to Machine (M2M) delay scenarios measuring the extra effort that they enforce to the transmitted data. Experimentation results reveal which protocol is best suited for different network and application scenarios accordingly.

  • 18.
    Musleh, Maath
    et al.
    TU Wien, Austria.
    Chatzimparmpas, Angelos
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Jusufi, Ilir
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Visual analysis of blow molding machine multivariate time series data2022Ingår i: Journal of Visualization, ISSN 1343-8875, E-ISSN 1875-8975, Vol. 25, s. 1329-1342Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. The tool is an interactive dashboard with multiple coordinated views supporting the exploration from both local and global perspectives. In summary, we investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners' requirements and follows the best practices within the field. We also conducted a case study with a domain expert to validate the potential application of the tool in the real-life context. Finally, we assessed the usability and usefulness of the tool with a two-layer summative evaluation that showed encouraging results.

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  • 19.
    Musleh, Maath
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Chatzimparmpas, Angelos
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Jusufi, Ilir
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Visual Analysis of Industrial Multivariate Time Series2021Ingår i: VINCI '21: Proceedings of the 14th International Symposium on Visual Information Communication and Interaction, ACM Press, 2021, artikel-id 3Konferensbidrag (Refereegranskat)
    Abstract [en]

    The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. We investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners' requirements and follows the best practices within the field.

  • 20.
    Papaefthimiou, Dimitra
    et al.
    University of Ioannina, Greece.
    Kontogiannis, Sotirios
    University of Ioannina, Greece.
    Chatzimparmpas, Angelos
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Kokkonis, George
    TEI of Western Macedonia, Greece.
    Valsamidis, Stavros
    TEI of East Macedonia and Thrace, Greece.
    Proposed OLEA management system with farming monitoring processes for virgin olive oil production traceability and assessment2019Ingår i: Innovative Approaches and Applications for Sustainable Rural Development: 8th International Conference, HAICTA 2017, Chania, Crete, Greece, September 21-24, 2017, Selected Papers / [ed] Theodoridis, Alexandros, Ragkos, Athanasios, Salampasis, Michail, Springer, 2019, s. 325-353Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    This paper proposes a cloud application architecture called OLEA, for monitoring the olive oil production chain. OLEA system deployment follows adivide–and-conquer management logic, which maintains olive tree clusters. On each cluster, NFC technology is used for monitoring plant protection practices and fertilization. Apart from on-site monitoring services, the system is also equipped with virgin oil management services. It uses an OLEA system controller that interconnects with sensors on oil mills, for the procurement of quantitative and qualitative olive oil characteristics, during the industrial extraction process. OLEAsystem services and management algorithms are controlled by a cloud application server, where collected data uploads and notifications are sent to the end users using a mobile phone application. This paper presents the OLEA system technical characteristics as well as the structure of OLEA communication protocols. Furthermore, a case study of the OLEA system data mining capabilities is presented examining the application of such efforts to the improvement of systematic cultivation, branding and product exports.

  • 21.
    Ploshchik, Ilya
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Chatzimparmpas, Angelos
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). Linköping University, Sweden.
    MetaStackVis: Visually-Assisted Performance Evaluation of Metamodels2023Ingår i: Proceedings of the 16th IEEE Pacific Visualization Symposium (PacificVis '23), visualization notes track, IEEE, 2023, IEEE, 2023, s. 207-211Konferensbidrag (Refereegranskat)
    Abstract [en]

    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 prob- lematic data instances. MetaStackVis was evaluated with a usage scenario based on a medical data set and via expert interviews.

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