Abstract

A new two-level ensemble regression method, as well as its modifications and application in applied problems, are considered. The key feature of the method is its focus on constructing an ensemble of predictors that approximate the target variable well and, at the same time, consist of algorithms that, if possible, differ from each other in terms of the calculated predictions. The ensemble with the indicated properties at the first stage is constructed through the optimization of a special functional, whose choice is theoretically substantiated in this study. At the second stage, a collective solution is calculated based on the forecasts formed by this ensemble. In addition, some heuristic modifications are described that have a positive effect on the quality of the forecast in applied problems. The effectiveness of the method is confirmed by the results obtained for specific applied problems.

中文翻译：

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评估物体真实特征的新两级机器学习方法

摘要

考虑了一种新的两级集成回归方法及其在应用问题中的修改和应用。该方法的关键特征是它专注于构建一个预测变量的集合，这些预测变量能够很好地近似目标变量，同时由算法组成，如果可能的话，这些算法在计算的预测方面彼此不同。第一阶段具有指定属性的系综是通过特殊泛函的优化来构建的，该泛函的选择在本研究中得到了理论上的证实。在第二阶段，根据该集合形成的预测计算集体解决方案。此外，还描述了一些启发式修改，这些修改对应用问题的预测质量具有积极影响。