Regression by Re-Ranking

Filipe Marcel Fernandes Gonçalves*, Daniel Carlos Guimarães Pedronette, Ricardo da Silva Torres

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

Several approaches based on regression have been developed in the past few years with the goal of improving prediction results, including the use of ranking strategies. Re-ranking has been exploited and successfully employed in several applications, improving rankings by encoding the manifold structure and redefining distances among elements from a dataset. Despite the promising results observed, re-ranking has not been evaluated in regressions tasks. This paper proposes a novel, generic, and customizable framework entitled Regression by Re-ranking (RbR), which explores the ability of re-ranking algorithms in determining relevant rankings of objects in prediction tasks. The framework relies on the integration of a base regressor, unsupervised re-ranking learning techniques, and predictions associated with nearest neighbours weighted according to their ranking positions. The RbR framework was evaluated under a rigorous experimental protocol and presented significant results in improving the prediction when compared to state-of-the-art approaches.

Original languageEnglish
Article number109577
JournalPattern Recognition
Volume140
DOIs
Publication statusPublished - Aug 2023

Keywords

  • Manifold
  • Prediction
  • Re-ranking
  • Regression
  • Unsupervised learning

Fingerprint

Dive into the research topics of 'Regression by Re-Ranking'. Together they form a unique fingerprint.

Cite this