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UBL: an R package for Utility-based Learning

arXiv 1604.08079 ↗First posted 27 Apr 2016cs.MS cs.LG stat.MLcited by 12

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Abstract (arXiv)

This document describes the R package UBL that allows the use of several methods for handling utility-based learning problems. Classification and regression problems that assume non-uniform costs and/or benefits pose serious challenges to predictive analytic tasks. In the context of meteorology, finance, medicine, ecology, among many other, specific domain information concerning the preference bias of the users must be taken into account to enhance the models predictive performance. To deal with this problem, a large number of techniques was proposed by the research community for both classification and regression tasks. The main goal of UBL package is to facilitate the utility-based predictive analytic task by providing a set of methods to deal with this type of problems in the R environment. It is a versatile tool that provides mechanisms to handle both regression and classification (binary and multiclass) tasks. Moreover, UBL package allows the user to specify his domain preferences, but it also provides some automatic methods that try to infer those preference bias from the domain, considering some common known settings.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Apr 2016.

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Citing papers per year: 2021: 2; 2022: 0; 2023: 3; 2024: 1; 2025: 3; 2026: 3.

12 papers.