FeatureInsight: Visual support for error-driven feature ideation in text classification

By Michael Brooks, Saleema Amershi, Bongshin Lee, Steven Mark Drucker, Ashish Kapoor, Patrice Y. Simard. 2015

94 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

FairSight: Visual Analytics for Fairness in Decision Making (2019, background): “For example, feature-level auditing methods seek to analyze the feature importance by permutation [11] or quantifying feature interaction [6, 13, 25].”
The challenge of crafting intelligible intelligence (2018, background): “ining examples, correct an erroneous label in existing data, specify a new feature, or change learning parameters and architecture. Here we expect to use affordances from interactive machine learning [4] and explanatory debugging [21]. To make these ideas concrete, Figure 6 illustrates a possible di”
Do Convolutional Neural Networks Learn Class Hierarchy? (2017, background): “Sample viewers are often integrated in machine learning environments to inspect individual samples along with their attributes and models responses [1, 10, 52].”
Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models (2018, background): “Typical solutions include improving hyperparameters [9,13], features [6,26,43], or training instances [29] and investigating multiple models in order to acquire an optimal model ensemble [35,38,43].”
Visual exploration of machine learning results using data cube analysis (2016, methodology): “Researchers have utilized features to explain how they affect models [12, 11, 10, 5]: Kulesza et al.”
A Review of User Interface Design for Interactive Machine Learning (2018, methodology): “FeatureInsight (Brooks et al. 2015) is a tool that enables feature selection in website classification.”
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models (2017, methodology): “Researchers have utilized features to visually describe how the models captured the structure of datasets [8, 20, 21, 23].”
Debugging Machine Learning Models (2016, background): “The work by Kulesza et al. (2010) provided explanations for certain types of errors, and several works have visually represented different model errors as in Brooks et al. (2015).”
Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models (2019, methodology): “Example tasks include interactive model debugging and performance analysis [2, 44, 53], feature ideation and selection [6, 34], instance subset inspection and comparison [30, 31], model comparison [67], and constructing interpretable and trustworthy visual analysis systems [11].”
Understanding Hidden Memories of Recurrent Neural Networks (2017, methodology): “These methods include: visual classiﬁcation [6,32,48], visual optimization [24,45], and visual feature engineering [4,41,49].”