A Comparison of Three Implementations of Multi-Label Conformal Prediction

Huazhen Wang, Xin Liu, Ilia Nouretdinov, Zhiyuan Luo

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The property of calibration of Multi-Label Learning (MLL) has not been well studied. Because of the excellent calibration property of Conformal Predictors (CP), it is valuable to achieve calibrated MLL prediction via CP. Three practical implementations of Multi-Label Conformal Predictors (MLCP) can be established. Among them are Instance Reproduction MLCP (IR-MLCP), Binary Relevance MLCP (BR-MLCP) and Power Set MLCP (PS-MLCP). The experimental results on benchmark datasets show that all three MLCP methods possess calibration property. Comparatively speaking, BR-MLCP performs better in terms of prediction efficiency and computational cost than the other two.
Original languageEnglish
Title of host publicationStatistical Learning and Data Sciences
Subtitle of host publicationThird International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings
EditorsAlex Gammerman, Vladimir Vovk, Harris Papadopoulos
Number of pages10
ISBN (Electronic)978-3-319-17091-6
ISBN (Print)978-3-319-17090-9
Publication statusPublished - 3 Apr 2015

Publication series

NameLecture Notes in Computer Science

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