Conformal Rule-Based Multi-label Classification

TL;DR

This paper explores how conformal prediction can improve rule-based multi-label classification by providing calibration for rule assessment, leading to better predictions and decision making.

Contribution

It introduces the integration of conformal prediction with rule learning in multi-label classification, highlighting mutual benefits and potential for improved calibration.

Findings

Conformal prediction enhances rule-based MLC by providing natural conformity scores.

Calibrated scores improve prediction accuracy and decision making.

Case study demonstrates practical usefulness in lazy rule learning.

Abstract

We advocate the use of conformal prediction (CP) to enhance rule-based multi-label classification (MLC). In particular, we highlight the mutual benefit of CP and rule learning: Rules have the ability to provide natural (non-)conformity scores, which are required by CP, while CP suggests a way to calibrate the assessment of candidate rules, thereby supporting better predictions and more elaborate decision making. We illustrate the potential usefulness of calibrated conformity scores in a case study on lazy multi-label rule learning.