190 citations · 681 across the 13 of their papers we have counts for
13 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…
DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine
Parisa Salmanian, Angelos Chatzimparmpas, Ali Can Karaca +1
Dimensionality Reduction (DR) techniques such as t-SNE and UMAP are popular for transforming complex datasets into simpler visual representations. However, while effective in uncov…
DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps
Angelos Chatzimparmpas, Rafael M. Martins, Alexandru C. Telea +1
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 tru…
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…
VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees
Angelos Chatzimparmpas, Rafael M. Martins, Andreas Kerren
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 met…
HUMAP: Hierarchical Uniform Manifold Approximation and Projection
Wilson E. Marcílio-Jr, Danilo M. Eler, Fernando V. Paulovich +1
Dimensionality reduction (DR) techniques help analysts to understand patterns in high-dimensional spaces. These techniques, often represented by scatter plots, are employed in dive…
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…
Using Visual Text Mining to Support the Study Selection Activity in Systematic Literature Reviews
Katia Romero Felizardo, Norsaremah Salleh, Rafael M. Martins +3
Background: A systematic literature review (SLR) is a methodology used to aggregate all relevant existing evidence to answer a research question of interest. Although crucial, the…
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.…
t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections
Angelos Chatzimparmpas, Rafael M. Martins, Andreas Kerren
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…
A Grid-based Method for Removing Overlaps of Dimensionality Reduction Scatterplot Layouts
Gladys M. Hilasaca, Wilson E. Marcílio-Jr, Danilo M. Eler +2
Dimensionality Reduction (DR) scatterplot layouts have become a ubiquitous visualization tool for analyzing multidimensional datasets. Despite their popularity, such scatterplots s…