A Survey of Visual Analytics Techniques for Machine Learning
arXiv:2008.09632
Abstract
Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the last ten years together with representative works before 2010. We build a taxonomy, which includes three first-level categories: techniques before model building, techniques during model building, and techniques after model building. Each category is further characterized by representative analysis tasks, and each task is exemplified by a set of recent influential works. We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.
References in corpus (21)
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- The What-If Tool: Interactive Probing of Machine Learning Models
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- Interactive Steering of Hierarchical Clustering
- Building Efficient ConvNets using Redundant Feature Pruning
- Diagnosing Concept Drift with Visual Analytics
- CrimAnalyzer: Understanding Crime Patterns in São Paulo City
- DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning
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