A Comparison of Three Implementations of Multi-Label Conformal Prediction

摘要

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.