10 papers · 1 filter
MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
Zeyang Huang, Takanori Fujiwara, Angelos Chatzimparmpas +2
We present a new nonlinear dimensionality reduction method, MAPLE, that enhances UMAP by improving manifold modeling. MAPLE employs a self-supervised learning approach to more effi…
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…
MetaStackVis: Visually-Assisted Performance Evaluation of Metamodels
Ilya Ploshchik, Angelos Chatzimparmpas, Andreas Kerren
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 se…
HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques
Angelos Chatzimparmpas, Fernando V. Paulovich, Andreas Kerren
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…
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…