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TCM-RF: Hedging the predictions of Random Forest

机译：TCM-RF：对冲随机森林的预测

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摘要

The output of traditional classifier is point prediction without giving any confidence of it. To the contrary, Transductive Confidence Machine (TCM), which is a novel framework that provides a prediction result coupled with its accurate confidence. This method also can hedge the prediction in which the predicting accuracy will be controlled by predefined confidence level. In the framework of TCM, the efficiency of prediction depends on the strangeness function of samples. This paper incorporates Random forests (RF) into the framework of TCM and proposes new TCM algorithm named TCM-RF, in which the strangeness obtained by RF will be used to implement the confidence prediction. Compared with traditional TCM algorithms, our method benefits from the more precise and robust strangeness measure and takes advantage of random forest. Experiments indicate its effectiveness and robustness. In addition, our study demonstrated that using ensemble strategies to define sample strangeness may be a more principled way than using a single classifier. On the other hand, it also shows that the paradigm of hedging prediction can be applied to an ensemble classifier.
机译：传统分类器的输出是点预测，而不会给予任何信心。对于相反的转换置信机（TCM），这是一种新颖的框架，其提供了一种预测结果，其耦合的准确置信度。该方法还可以对冲预测精度将通过预定义的置信水平来控制预测。在TCM的框架中，预测效率取决于样品的奇异功能。本文将随机森林（RF）融入了TCM的框架中，并提出了名为TCM-RF的新型TCM算法，其中RF获得的陌生性将用于实施置信度预测。与传统的TCM算法相比，我们的方法从更精确和强大的奇特度量中受益，利用随机森林。实验表明其有效性和鲁棒性。此外，我们的研究表明，使用集合策略来定义样本奇异可能是比使用单个分类器更具原则的方式。另一方面，它还表明可以将对冲预测的范例应用于集合分类器。

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