Title | ||
---|---|---|
BEAMES: Interactive Multi-Model Steering, Selection, and Inspection for Regression Tasks. |
Abstract | ||
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Interactive model steering helps people incrementally build machine learning models that are tailored to their domain and task. Existing visual analytic tools allow people to steer a single model (e.g., assignment attribute weights used by a dimension reduction model). However, the choice of model is critical in such situations. What if the model chosen is sub-optimal for the task, dataset, or question being asked What if instead of parameterizing and steering this model, a different model provides a better fit This paper presents a technique to allow users to inspect and steer multiple machine learning models. The technique steers and samples models from a broader set of learning algorithms and model types. We incorporate this technique into a visual analytic prototype, BEAMES, that allows users to perform regression tasks via multi-model steering. This paper demonstrates the effectiveness of BEAMES via a use case, and discusses broader implications for multi-model steering. |
Year | DOI | Venue |
---|---|---|
2019 | 10.1109/MCG.2019.2922592 | IEEE computer graphics and applications |
Keywords | Field | DocType |
Data models,Computational modeling,Analytical models,Task analysis,Visual analytics,Inspection,Machine learning | Data modeling,Computer vision,Dimensionality reduction,Task analysis,Regression,Computer science,Visual analytics,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
39 | 5 | 1558-1756 |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Subhajit Das | 1 | 13 | 6.22 |
Dylan Cashman | 2 | 10 | 3.11 |
Remco Chang | 3 | 983 | 64.96 |
Alex Endert | 4 | 974 | 52.18 |