UBL: an R package for Utility-based Learning
Not on the map. Why?
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.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 0; 2023: 3; 2024: 1; 2025: 3; 2026: 3.
12 papers.
- 2021DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testingcited by 8
- 2023Data Augmentation for Imbalanced Regressioncited by 7
- 2021Two Shifts for Crop Mapping: Leveraging Aggregate Crop Statistics to Improve Satellite-based Maps in New Regionscited by 6
- 2024Data Augmentation with Variational Autoencoder for Imbalanced Datasetcited by 5
- 2023Generalized Oversampling for Learning from Imbalanced datasets and Associated Theorycited by 4
- 2025Resampling strategies for imbalanced regression: a survey and empirical analysiscited by 3
- 2025Imbalanced Regression Pipeline Recommendationcited by 1
- 2025Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regressioncited by 1
- 2023Noise-in, Bias-out: Balanced and Real-time MoCap Solvingcited by 0
- 2026DADIR: Density-Aware Data-level Imbalanced Regression Frameworkcited by 0
- 2026Instance Hardness-Based Relevance for Imbalanced Regressioncited by 0
- 2026Mind the Long Tail: Understanding the Difficulty of Delay Detection in Business Processescited by 0
- 2026Mind the Long Tail: Understanding the Difficulty of Delay Detection in Business Processes14 Aug 2026
- 2026Instance Hardness-Based Relevance for Imbalanced Regression22 Jul 2026
- 2026DADIR: Density-Aware Data-level Imbalanced Regression Framework19 Jul 2026
- 2025Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression19 Aug 2025
- 2025Resampling strategies for imbalanced regression: a survey and empirical analysis16 Jul 2025
- 2025Imbalanced Regression Pipeline Recommendation16 Jul 2025
- 2024Data Augmentation with Variational Autoencoder for Imbalanced Dataset9 Dec 2024
- 2023Noise-in, Bias-out: Balanced and Real-time MoCap Solving25 Sep 2023
- 2023Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory5 Aug 2023
- 2023Data Augmentation for Imbalanced Regression18 Feb 2023
- 2021DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing14 Oct 2021
- 2021Two Shifts for Crop Mapping: Leveraging Aggregate Crop Statistics to Improve Satellite-based Maps in New Regions2 Sep 2021