DeepAI AI Chat
Log In Sign Up

Conformal Rule-Based Multi-label Classification

by   Eyke Hüllermeier, et al.

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.


page 1

page 2

page 3

page 4


Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in whi...

Improved Multi-label Classification with Frequent Label-set Mining and Association

Multi-label (ML) data deals with multiple classes associated with indivi...

A Cross-Conformal Predictor for Multi-label Classification

Unlike the typical classification setting where each instance is associa...

Learning Structured Declarative Rule Sets – A Challenge for Deep Discrete Learning

Arguably the key reason for the success of deep neural networks is their...

Quantifying the Uncertainty of Precision Estimates for Rule based Text Classifiers

Rule based classifiers that use the presence and absence of key sub-stri...