Unlabeled multi-target regression with genetic programming

Uriel Lopez, Leonardo Trujillo, Sara Silva, Leonardo Vanneschi, Pierrick Legrand

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)
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Machine Learning (ML) has now become an important and ubiquitous tool in science and engineering, with successful applications in many real-world domains. However, there are still areas in need of improvement, and problems that are still considered difficult with off-the-shelf methods. One such problem is Multi Target Regression (MTR), where the target variable is a multidimensional tuple instead of a scalar value. In this work, we propose a more difficult variant of this problem which we call Unlabeled MTR (uMTR), where the structure of the target space is not given as part of the training data. This version of the problem lies at the intersection of MTR and clustering, an unexplored problem type. Moreover, this work proposes a solution method for uMTR, a hybrid algorithm based on Genetic Programming and RANdom SAmple Consensus (RANSAC). Using a set of benchmark problems, we are able to show that this approach can effectively solve the uMTR problem.

Original languageEnglish
Title of host publicationGECCO 2020
Subtitle of host publicationProceedings of the 2020 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery
Number of pages9
ISBN (Electronic)9781450371285
Publication statusPublished - 25 Jun 2020
Event2020 Genetic and Evolutionary Computation Conference, GECCO 2020 - Cancun, Mexico
Duration: 8 Jul 202012 Jul 2020

Publication series

NameGECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference


Conference2020 Genetic and Evolutionary Computation Conference, GECCO 2020


  • Clustering
  • Genetic programming
  • Multi-target regression
  • Unlabeled multi-target regression


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