TY - JOUR
T1 - Regression by Re-Ranking
AU - Gonçalves, Filipe Marcel Fernandes
AU - Pedronette, Daniel Carlos Guimarães
AU - da Silva Torres, Ricardo
PY - 2023/8
Y1 - 2023/8
N2 - 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.
AB - 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.
KW - Manifold
KW - Prediction
KW - Re-ranking
KW - Regression
KW - Unsupervised learning
U2 - 10.1016/j.patcog.2023.109577
DO - 10.1016/j.patcog.2023.109577
M3 - Article
AN - SCOPUS:85151620538
SN - 0031-3203
VL - 140
JO - Pattern Recognition
JF - Pattern Recognition
M1 - 109577
ER -