Regression by Re-Ranking

• Autor(a) principal: Gonçalves, Filipe Marcel Fernandes
• Data de Publicação: 2023
• Outros Autores: Pedronette, Daniel Carlos Guimarães [UNESP], da Silva Torres, Ricardo
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.patcog.2023.109577; http://hdl.handle.net/11449/247111
• topic: Manifold; Prediction; Re-ranking; Regression; Unsupervised learning

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.