Name
Abstract | ||
|---|---|---|
Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack in comprehensibility, posing a challenge to understand how each model affects the classification outputs and where the errors come from. We propose a tight visual integration of the data and the model space for exploring and combining classifier models. We introduce a workflow that builds upon the visual integration and enables the effective exploration of classification outputs and models. We then present a use case in which we start with an ensemble automatically selected by a standard ensemble selection algorithm, and show how we can manipulate models and alternative combinations. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/VDS.2017.8573444 | 2017 IEEE Visualization in Data Science (VDS) |
Keywords | Field | DocType |
Pattern Recognition [I.5.2]: Design Methodology-Classifier design and evaluation | Ensemble selection,Computer science,Artificial intelligence,Classifier (linguistics),Workflow,Ensemble learning,Machine learning | Journal |
Volume | ISBN | Citations |
abs/1710.07322 | 978-1-5386-3186-7 | 2 |
PageRank | References | Authors |
0.37 | 23 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bruno Schneider | 1 | 4 | 2.75 |
| dominik jackle | 2 | 58 | 8.66 |
| Florian Stoffel | 3 | 106 | 9.38 |
| Alexandra Diehl | 4 | 2 | 0.71 |
| johannes fuchs | 5 | 17 | 3.45 |
| Daniel A. Keim | 6 | 7704 | 1141.60 |