# Multi-Target Regression via Random Linear Target Combinations

arXiv: https://arxiv.org/abs/1404.5065 · HTML: https://mlsanity.com/paper/1404.5065/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2014.

## Built on by (most cited first, 4 of 4)

- [Local Explanation of Dimensionality Reduction](https://mlsanity.com/paper/2204.14012/) (2022)
- [From Multi-label Learning to Cross-Domain Transfer: A Model-Agnostic Approach](https://mlsanity.com/paper/2207.11742/) (2022)
- [Multivariate Uncertainty Quantification with Tomographic Quantile Forests](https://mlsanity.com/paper/2512.16383/) (2025)
- [Hybrid Predictive Modeling of Malaria Incidence in the Amhara Region, Ethiopia: Integrating Multi-Output Regression and Time-Series Forecasting](https://mlsanity.com/paper/2510.01302/) (2025)

## Abstract (arXiv)

Multi-target regression is concerned with the simultaneous prediction of multiple continuous target variables based on the same set of input variables. It arises in several interesting industrial and environmental application domains, such as ecological modelling and energy forecasting. This paper presents an ensemble method for multi-target regression that constructs new target variables via random linear combinations of existing targets. We discuss the connection of our approach with multi-label classification algorithms, in particular RAkEL, which originally inspired this work, and a family of recent multi-label classification algorithms that involve output coding. Experimental results on 12 multi-target datasets show that it performs significantly better than a strong baseline that learns a single model for each target using gradient boosting and compares favourably to multi-objective random forest approach, which is a state-of-the-art approach. The experiments further show that our approach improves more when stronger unconditional dependencies exist among the targets.