Visual Analytics for Machine Learning: A Data Perspective Survey

The past decade has witnessed a plethora of works that leverage the power of visualization (VIS) to interpret machine learning (ML) models. The corresponding research topic, VIS4ML, keeps growing at a fast pace. To better organize the enormous works and shed light on the developing trend of VIS4ML,...

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Vydáno v:IEEE transactions on visualization and computer graphics Ročník 30; číslo 12; s. 7637 - 7656
Hlavní autoři: Wang, Junpeng, Liu, Shixia, Zhang, Wei
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.12.2024
Témata:
Analytical models
Data models
Explainable AI
machine learning
Surveys
Task analysis
Taxonomy
VIS4ML
Visual analytics
visualization
ISSN:1077-2626, 1941-0506, 1941-0506
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Shrnutí:The past decade has witnessed a plethora of works that leverage the power of visualization (VIS) to interpret machine learning (ML) models. The corresponding research topic, VIS4ML, keeps growing at a fast pace. To better organize the enormous works and shed light on the developing trend of VIS4ML, we provide a systematic review of these works through this survey. Since data quality greatly impacts the performance of ML models, our survey focuses specifically on summarizing VIS4ML works from the data perspective . First, we categorize the common data handled by ML models into five types, explain the unique features of each type, and highlight the corresponding ML models that are good at learning from them. Second, from the large number of VIS4ML works, we tease out six tasks that operate on these types of data (i.e., data-centric tasks) at different stages of the ML pipeline to understand, diagnose, and refine ML models. Lastly, by studying the distribution of 143 surveyed papers across the five data types, six data-centric tasks, and their intersections, we analyze the prospective research directions and envision future research trends.
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2024.3357065