Unsupervised Weight Parameter Estimation Method for Ensemble Learning
Unsupervised Weight Parameter Estimation Method for Ensemble Learning
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DOI：
10.1007/s10852-011-9157-1
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发表时间：
2011-12
期刊：
影响因子：
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通讯作者：
M. Uchida;Y. Maehara;H. Shioya
中科院分区：
文献类型：
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作者：
M. Uchida;Y. Maehara;H. Shioya
当有多个经过训练的预测器时，可能需要将它们集成到一个预测器中。然而，如果训练的预测器的性能是未知的，并且没有给出用于评估其性能的标记数据，这是具有挑战性的。本文描述了一种利用未标记数据估计集成多个训练分量预测器所需的权重参数的方法。它很容易从集成学习的数学模型中得到，该集成学习基于概率密度函数和相应的信息发散度量的广义混合。数值实验表明，即使在对分量预报器性能的假设不完全成立的情况下，我们的方法的性能也比简单的基于平均的集成学习好得多。
When there are multiple trained predictors, one may want to integrate them into one predictor. However, this is challenging if the performances of the trained predictors are unknown and labeled data for evaluating their performances are not given. In this paper, a method is described that uses unlabeled data to estimate the weight parameters needed to build an ensemble predictor integrating multiple trained component predictors. It is readily derived from a mathematical model of ensemble learning based on a generalized mixture of probability density functions and corresponding information divergence measures. Numerical experiments demonstrated that the performance of our method is much better than that of simple average-based ensemble learning, even when the assumption placed on the performances of the component predictors does not hold exactly.