190 citations · 423 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…