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MetaUtil: Meta Learning for Utility Maximization in Regression

机译:MetaUtil:用于回归中效用最大化的元学习

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摘要

Several important real world problems of predictive analytics involve handling different costs of the predictions of the learned models. The research community has developed multiple techniques to deal with these tasks. The utility-based learning framework is a generalization of cost-sensitive tasks that takes into account both costs of errors and benefits of accurate predictions. This framework has important advantages such as allowing to represent more complex settings reflecting the domain knowledge in a more complete and precise way. Most existing work addresses classification tasks with only a few proposals tackling regression problems. In this paper we propose a new method, MetaUtil, for solving utility-based regression problems. The MetaUtil algorithm is versatile allowing the conversion of any out-of-the-box regression algorithm into a utility-based method. We show the advantage of our proposal in a large set of experiments on a diverse set of domains.
机译:现实世界中预测分析的几个重要问题涉及处理学习模型的预测的不同成本。研究界已经开发出多种技术来处理这些任务。基于实用程序的学习框架是对成本敏感型任务的概括,它同时考虑了错误成本和准确预测的好处。该框架具有重要的优势,例如允许代表更复杂的设置,以更完整和精确的方式反映领域知识。现有的大多数工作都只针对解决回归问题的一些提案来解决分类任务。在本文中,我们提出了一种新的方法MetaUtil,用于解决基于效用的回归问题。 MetaUtil算法用途广泛,可以将任何现成的回归算法转换为基于实用程序的方法。我们在一系列不同领域的大量实验中展示了我们建议的优势。

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  • 来源
    《Discovery science》|2018年|129-143|共15页
  • 会议地点 Limassol(CY)
  • 作者单位

    LIAAD - INESC TEC, Porto, Portugal,DCC - Faculdade de Ciencias, Universidade do Porto, Porto, Portugal;

    LIAAD - INESC TEC, Porto, Portugal,DCC - Faculdade de Ciencias, Universidade do Porto, Porto, Portugal,Faculty of Computer Science, Dalhousie University, Halifax, Canada;

    LIAAD - INESC TEC, Porto, Portugal,DCC - Faculdade de Ciencias, Universidade do Porto, Porto, Portugal;

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  • 原文格式 PDF
  • 正文语种 eng
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