CP-RAkEL: Improving Random k-labelsets with Conformal Prediction for Multi-label Classification

Multi-label conformal prediction has attracted much attention in the conformal predictor (CP) community. In this article, we propose to combine CP with random k -labelsets (RAkEL) method, which is state-of-the-art multi-label classification method for large number of labels. In the framework of RAkEL, the original problem is reduced to a number of small-sized multi-label classification tasks by randomly breaking the initial set of labels into a number of small-sized labelsets, and then label powerset (LP) method is employed on these tasks respectively. In this work, ICP-RF, an inductive conformal predictor based on random forest, is used in each multi-label task in order to get p-values for predictions of the LP model, and then the predictions are aggregated to get a final result. Experimental results on six benchmark datasets empirically demonstrate the calibration property of ICP-RF as LP models, and show that conformal prediction can significantly improve the performances of the proposed approach, which is called RAkEL. However, the validity property of CP does not hold in CP-RAkEL. In the future work we will study how to use some new CP techniques to calibrate the new method.