@inproceedings{10714294,

title = {Visual Analytics for Interactive Machine Learning - A Modular Multi-View Approach},

author = {Elena Correll and Uliana Eliseeva and Kawa Nazemi},

doi = {10.1109/IV64223.2024.00036},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {2024 28th International Conference Information Visualisation (IV)},

pages = {1-7},

abstract = {Interactive machine learning enhances data pre-processing, feature engineering, and modeling through human interactions. As interactive machine learning becomes more in-corporated into visual analytics, new pipelines, concepts, and task taxonomies rise. A common technique for managing complexity in visual analytic systems is multiple-linked views. By now no comprehensive concept exists for multiple-linked views across all stages of the visual analytic pipeline. Therefore, we introduce a conceptual model that shows the possible interactions between humans and computers at each step of the VA pipeline. Our main contribution is a coordination model for linking multiple views across each layer of the VA pipeline.},

keywords = {Computers;Analytical models;Visual analytics;Computational modeling;Pipelines;Taxonomy;Refining;Decision making;Machine learning;Data models;Visual Analytics;Interactive Machine Learning;Multiple-Linked Views},

pubstate = {published},

tppubtype = {inproceedings}

}