Multiclass Performance Metric Elicitation
Gaurush Hiranandani ⋅ Shant Boodaghians ⋅ Ruta Mehta ⋅ Sanmi Koyejo
Keywords:
Learning Theory
Theory
Algorithms -> Active Learning; Algorithms -> Classification; Algorithms
Ranking and Preference Learning
2019 Poster
Abstract
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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