Regression by Re-Ranking. (August 2023) - Gonçalves, Filipe Marcel Fernandes; Pedronette, Daniel Carlos Guimarães; da Silva Torres, Ricardo
Highlights: A novel rank-based approach is proposed for regression tasks; The method allows to combine predictions from a base regressor and neighborhood information; Re-ranking strategies and weighted functions are used for better exploiting neighborhood information; Several experiments considering various datasets, base regressors and re-ranking methods; Significant decrease of prediction errors considering traditional and state-of-the-art regressors. 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.