# UBL: an R package for Utility-based Learning

arXiv: https://arxiv.org/abs/1604.08079 · HTML: https://mlsanity.com/paper/1604.08079/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2016.

## Built on by (most cited first, 12 of 12)

- [DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing](https://mlsanity.com/paper/2110.07443/) (2021)
- [Data Augmentation for Imbalanced Regression](https://mlsanity.com/paper/2302.09288/) (2023)
- [Two Shifts for Crop Mapping: Leveraging Aggregate Crop Statistics to Improve Satellite-based Maps in New Regions](https://mlsanity.com/paper/2109.01246/) (2021)
- [Data Augmentation with Variational Autoencoder for Imbalanced Dataset](https://mlsanity.com/paper/2412.07039/) (2024)
- [Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory](https://mlsanity.com/paper/2308.02966/) (2023)
- [Resampling strategies for imbalanced regression: a survey and empirical analysis](https://mlsanity.com/paper/2507.11902/) (2025)
- [Imbalanced Regression Pipeline Recommendation](https://mlsanity.com/paper/2507.11901/) (2025)
- [Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression](https://mlsanity.com/paper/2508.13829/) (2025)
- [Noise-in, Bias-out: Balanced and Real-time MoCap Solving](https://mlsanity.com/paper/2309.14330/) (2023)
- [DADIR: Density-Aware Data-level Imbalanced Regression Framework](https://mlsanity.com/paper/2607.17178/) (2026)
- [Instance Hardness-Based Relevance for Imbalanced Regression](https://mlsanity.com/paper/2607.20173/) (2026)
- [Mind the Long Tail: Understanding the Difficulty of Delay Detection in Business Processes](https://mlsanity.com/paper/2608.14367/) (2026)

## 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.