Poster
Multiclass Performance Metric Elicitation
Gaurush Hiranandani · Shant Boodaghians · Ruta Mehta · Sanmi Koyejo
East Exhibition Hall B, C #226
Keywords: [ Learning Theory ] [ Theory ] [ Algorithms -> Active Learning; Algorithms -> Classification; Algorithms ] [ Ranking and Preference Learning ]
Metric Elicitation is a principled framework for selecting the performance metric that best reflects implicit user preferences. However, available strategies have so far been limited to binary classification. In this paper, we propose novel strategies for eliciting multiclass classification performance metrics using only relative preference feedback. We also show that the strategies are robust to both finite sample and feedback noise.
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