Regression by Re-Ranking

Author(s)

Goncalves, Filipe Marcel Fernandes ; Pedronette, Daniel Carlos Guimaraes ; Torres, Ricardo da Silva

• Document type: Journal article
• Source: PATTERN RECOGNITION; v. 140, p. 17-pg., 2023-04-09.

Abstract

Several approaches based on regression have been developed in the past few years with the goal of im-proving prediction results, including the use of ranking strategies. Re-ranking has been exploited and suc-cessfully employed in several applications, improving rankings by encoding the manifold structure and re-defining 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 frame-work entitled Regression by Re-ranking (RbR), which explores the ability of re-ranking algorithms in deter-mining 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 neigh-bours 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.(c) 2023 Elsevier Ltd. All rights reserved. (AU)

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