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Bad and good errors: value-weighted skill scores in deep ensemble learning
arXiv - CS - Machine Learning Pub Date : 2021-03-04 , DOI: arxiv-2103.02881 Sabrina Guastavino, Michele Piana, Federico Benvenuto
arXiv - CS - Machine Learning Pub Date : 2021-03-04 , DOI: arxiv-2103.02881 Sabrina Guastavino, Michele Piana, Federico Benvenuto
In this paper we propose a novel approach to realize forecast verification.
Specifically, we introduce a strategy for assessing the severity of forecast
errors based on the evidence that, on the one hand, a false alarm just
anticipating an occurring event is better than one in the middle of consecutive
non-occurring events, and that, on the other hand, a miss of an isolated event
has a worse impact than a miss of a single event, which is part of several
consecutive occurrences. Relying on this idea, we introduce a novel definition
of confusion matrix and skill scores giving greater importance to the value of
the prediction rather than to its quality. Then, we introduce a deep ensemble
learning procedure for binary classification, in which the probabilistic
outcomes of a neural network are clustered via optimization of these
value-weighted skill scores. We finally show the performances of this approach
in the case of three applications concerned with pollution, space weather and
stock prize forecasting.
中文翻译:
好的和坏的错误:深度合奏学习中的价值加权技能得分
在本文中,我们提出了一种新颖的方法来实现预测验证。具体而言,我们基于以下证据引入了一种评估预测错误的严重性的策略:一方面,仅预测发生事件的虚假警报要比连续发生的非发生事件中的虚假警报要好。另一方面,遗漏孤立事件比遗漏单个事件的影响更严重,后者是几次连续事件的一部分。依靠这个想法,我们引入了混淆矩阵和技能得分的新颖定义,它更加重视预测的价值而不是预测的质量。然后,我们介绍了用于二元分类的深度集成学习程序,其中,神经网络的概率结果是通过优化这些价值加权技能得分而聚类的。我们最终将在涉及污染,太空天气和股票报酬预测的三个应用程序的情况下展示该方法的性能。
更新日期:2021-03-05
中文翻译:
好的和坏的错误:深度合奏学习中的价值加权技能得分
在本文中,我们提出了一种新颖的方法来实现预测验证。具体而言,我们基于以下证据引入了一种评估预测错误的严重性的策略:一方面,仅预测发生事件的虚假警报要比连续发生的非发生事件中的虚假警报要好。另一方面,遗漏孤立事件比遗漏单个事件的影响更严重,后者是几次连续事件的一部分。依靠这个想法,我们引入了混淆矩阵和技能得分的新颖定义,它更加重视预测的价值而不是预测的质量。然后,我们介绍了用于二元分类的深度集成学习程序,其中,神经网络的概率结果是通过优化这些价值加权技能得分而聚类的。我们最终将在涉及污染,太空天气和股票报酬预测的三个应用程序的情况下展示该方法的性能。