Efficient set-valued prediction in multi-class classification

DOI10.1007/s10618-021-00751-xzbMath1478.62173arXiv1906.08129OpenAlexW3159075814MaRDI QIDQ2036773

Thomas Mortier, Krzysztof Dembczyński, Willem Waegeman, Marek Wydmuch, Eyke Hüllermeier

Publication date: 30 June 2021

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1906.08129

zbMATH Keywords

multi-class classificationexpected utility maximizationset-valued prediction

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Uses Software

• LIBLINEAR
• MNIST
• word2vec
• PyTorch
• FastXML
• DiSMEC
• Caltech-256
• ALOI
• PASCAL VOC

Cites Work

• Reliable classification: learning classifiers that distinguish aleatoric and epistemic uncertainty
• On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis
• Consistent algorithms for multiclass classification with an abstain option
• Exact and efficient top-\(K\) inference for multi-target prediction by querying separable linear relational models
• A family of admissible heuristics for \(\mathrm{A}^*\) to perform inference in probabilistic classifier chains
• Evaluating credal classifiers by utility-discounted predictive accuracy
• Confidence sets with expected sizes for Multiclass Classification
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Efficient set-valued prediction in multi-class classification