The aim of this article is to study the relationship between two popular Cautious Learning approaches, namely: Three-way decision (TWD) and conformal prediction (CP). Based on the novel proposal of a technique to transform three-way decision classifiers into conformal predictors, and vice versa, we provide conditions for the equivalence between TWD and CP. These theoretical results provide error-bound guarantees for TWD, together with a formal construction to define cost-sensitive cautious classifiers based on CP. The proposed techniques are then applied and evaluated on a collection of benchmark and real-world datasets. The results of the experiments show that the proposed techniques can be used to obtain cautious learning classifiers that are competitive with, and often out-perform, state-of-the-art approaches. Further, through a qualitative medical case study we discuss the usefulness of cautious learning in the development of robust Machine Learning.
Campagner, A., Cabitza, F., Berjano, P., Ciucci, D. (2021). Three-way decision and conformal prediction: Isomorphisms, differences and theoretical properties of cautious learning approaches. INFORMATION SCIENCES, 579(November 2021), 347-367 [10.1016/j.ins.2021.08.009].
Three-way decision and conformal prediction: Isomorphisms, differences and theoretical properties of cautious learning approaches
Campagner A.
;Cabitza F.;Ciucci D.
2021
Abstract
The aim of this article is to study the relationship between two popular Cautious Learning approaches, namely: Three-way decision (TWD) and conformal prediction (CP). Based on the novel proposal of a technique to transform three-way decision classifiers into conformal predictors, and vice versa, we provide conditions for the equivalence between TWD and CP. These theoretical results provide error-bound guarantees for TWD, together with a formal construction to define cost-sensitive cautious classifiers based on CP. The proposed techniques are then applied and evaluated on a collection of benchmark and real-world datasets. The results of the experiments show that the proposed techniques can be used to obtain cautious learning classifiers that are competitive with, and often out-perform, state-of-the-art approaches. Further, through a qualitative medical case study we discuss the usefulness of cautious learning in the development of robust Machine Learning.File | Dimensione | Formato | |
---|---|---|---|
paper_prepublication.pdf
accesso aperto
Tipologia di allegato:
Submitted Version (Pre-print)
Dimensione
2.48 MB
Formato
Adobe PDF
|
2.48 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.