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.
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Notes
- 1.
is the indicator function, i.e., 
if the predicate A is true and \(=0\) otherwise. - 2.
50 random splits into 400 training examples and 196 test examples.
- 3.
Note that the accuracy-rejection curve for random abstention is flat.
is the indicator function, i.e., 
if the predicate A is true and References
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This work was supported by the German Research Foundation (DFG) under grant number 400845550.
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Hüllermeier, E., Fürnkranz, J., Loza Mencia, E. (2020). Conformal Rule-Based Multi-label Classification. In: Schmid, U., Klügl, F., Wolter, D. (eds) KI 2020: Advances in Artificial Intelligence. KI 2020. Lecture Notes in Computer Science(), vol 12325. Springer, Cham. https://doi.org/10.1007/978-3-030-58285-2_25
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