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Regression by Re-Ranking
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DOI：10.1016/j.patcog.2023.109577.png)
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摘要
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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.
Keyword:
Regression
Re-ranking
Prediction
Manifold
Unsupervised learning
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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