no code implementations • 4 Sep 2024 • Takanori Fujiwara, Kostiantyn Kucher, Junpeng Wang, Rafael M. Martins, Andreas Kerren, Anders Ynnerman
Research in ML4VIS investigates how to use machine learning (ML) techniques to generate visualizations, and the field is rapidly growing with high societal impact.
no code implementations • 18 Mar 2024 • Angelos Chatzimparmpas, Kostiantyn Kucher, Andreas Kerren
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.
no code implementations • 23 Sep 2022 • Kostiantyn Kucher, Nicole Sultanum, Angel Daza, Vasiliki Simaki, Maria Skeppstedt, Barbara Plank, Jean-Daniel Fekete, Narges Mahyar
We identify four key groups of challenges for evaluating visual text analytics approaches (data ambiguity, experimental design, user trust, and "big picture" concerns) and provide suggestions for research opportunities from an interdisciplinary perspective.
no code implementations • 26 Mar 2021 • Angelos Chatzimparmpas, Rafael M. Martins, Kostiantyn Kucher, Andreas Kerren
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.
1 code implementation • 2 Dec 2020 • Angelos Chatzimparmpas, Rafael M. Martins, Kostiantyn Kucher, Andreas Kerren
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.
1 code implementation • 4 May 2020 • Angelos Chatzimparmpas, Rafael M. Martins, Kostiantyn Kucher, Andreas Kerren
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.
no code implementations • 8 Mar 2020 • Tácito T. A. T. Neves, Rafael M. Martins, Danilo B. Coimbra, Kostiantyn Kucher, Andreas Kerren, Fernando V. Paulovich
To the best of our knowledge, it is the first methodology that is capable of evolving a projection to faithfully represent new emerging structures without the need to store all data, providing reliable results for efficiently and effectively projecting streaming data.