Conformal Association Rule Mining (CARM): A novel technique for data error detection and probabilistic correction

Ilia Nouretdinov, James Gammerman
Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 204:267-286, 2023.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v204-nouretdinov23a, title = {Conformal Association Rule Mining (CARM): A novel technique for data error detection and probabilistic correction}, author = {Nouretdinov, Ilia and Gammerman, James}, booktitle = {Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {267--286}, year = {2023}, editor = {Papadopoulos, Harris and Nguyen, Khuong An and Boström, Henrik and Carlsson, Lars}, volume = {204}, series = {Proceedings of Machine Learning Research}, month = {13--15 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v204/nouretdinov23a/nouretdinov23a.pdf}, url = {https://proceedings.mlr.press/v204/nouretdinov23a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Conformal Association Rule Mining (CARM): A novel technique for data error detection and probabilistic correction %A Ilia Nouretdinov %A James Gammerman %B Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2023 %E Harris Papadopoulos %E Khuong An Nguyen %E Henrik Boström %E Lars Carlsson %F pmlr-v204-nouretdinov23a %I PMLR %P 267--286 %U https://proceedings.mlr.press/v204/nouretdinov23a.html %V 204 %X 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.
APA
Nouretdinov, I. & Gammerman, J.. (2023). Conformal Association Rule Mining (CARM): A novel technique for data error detection and probabilistic correction. Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 204:267-286 Available from https://proceedings.mlr.press/v204/nouretdinov23a.html.

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