InterActive Feature Selection

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Training Classifiers with Natural Language Explanations (2018, background): “Previous works have relied on identifying relevant parts of the input such as labeling features (Druck et al., 2009; Raghavan et al., 2005; Liang et al., 2009), highlighting rationale phrases in Both cohorts showed signs of optic nerve toxicity due to ethambutol.”
FeatureInsight: Visual support for error-driven feature ideation in text classification (2015, background): “Others have explored enabling users to adjust feature weights [15], [21], interactively debugging features [22], and crowd-generating features [16].”
Using “Annotator Rationales” to Improve Machine Learning for Text Categorization (2007, background): “Raghavan et al. (2005) asked annotators to identify globally “relevant” features.”
Active Learning with Feedback on Features and Instances (2006, background): “This paper extends our previous work in employing such a two-tiered approach to active learning (Raghavan et al., 2005).”
Interactive Machine Learning via a GPU-accelerated Toolkit (2017, background): “Other work focused on human-assisted feature selection (rather than algorithm training) [31].”
Annotator rationales for visual recognition (2011, background): “Work in natural language processing (NLP) explores whether humans can pick out words relevant for a given document category as a form of human feature selection [6, 18, 28].”