190 citations · 423 across the 8 of their papers we have counts for
8 papers
Adversarial Attacks on Machine Learning-Aided Visualizations
Takanori Fujiwara, Kostiantyn Kucher, Junpeng Wang +3
Research in ML4VIS investigates how to use machine learning (ML) techniques to generate visualizations, and the field is rapidly growing with high societal impact. However, as with…
Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023
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…
The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
A. Chatzimparmpas, R. Martins, I. Jusufi +3
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, howe…
An Interdisciplinary Perspective on Evaluation and Experimental Design for Visual Text Analytics: Position Paper
Kostiantyn Kucher, Nicole Sultanum, Angel Daza +5
Appropriate evaluation and experimental design are fundamental for empirical sciences, particularly in data-driven fields. Due to the successes in computational modeling of languag…
FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches
Angelos Chatzimparmpas, Rafael M. Martins, Kostiantyn Kucher +1
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…
VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization
Angelos Chatzimparmpas, Rafael M. Martins, Kostiantyn Kucher +1
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 e…
StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics
Angelos Chatzimparmpas, Rafael M. Martins, Kostiantyn Kucher +1
In machine learning (ML), ensemble methods such as bagging, boosting, and stacking are widely-established approaches that regularly achieve top-notch predictive performance. Stacki…
Xtreaming: an incremental multidimensional projection technique and its application to streaming data
Tácito T. A. T. Neves, Rafael M. Martins, Danilo B. Coimbra +3
Streaming data applications are becoming more common due to the ability of different information sources to continuously capture or produce data, such as sensors and social media.…