2023-08-17-nouretdinov23a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Conformal Association Rule Mining (CARM): A novel technique for data error detection and probabilistic correction	Conformal prediction (CP) is a modern framework for reliable machine learning. It is most commonly used in the context of supervised learning, where in combination with an underlying algorithm it generates predicted labels for new, unlabelled examples and complements each of them with an individual measure of confidence. Conversely, association rule mining (ARM) is an unsupervised learning technique for discovering interesting relationships in large datasets in the form of rules. In this work, we integrate CP and ARM to develop a novel technique termed Conformal Association Rule Mining (CARM). The technique enables the identification of probable errors within a set of binary labels. Subsequently, these probable errors are analysed using another modern framework called Venn-ABERS prediction to correct the value in a probabilistic way.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	nouretdinov23a	0	Conformal Association Rule Mining (CARM): A novel technique for data error detection and probabilistic correction	267	286	267-286	267	false	Nouretdinov, Ilia and Gammerman, James	given family Ilia Nouretdinov given family James Gammerman	2023-08-17		Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications	204	inproceedings	date-parts 2023 8 17	https://proceedings.mlr.press/v204/nouretdinov23a/nouretdinov23a.pdf