Feature selection is one of the most relevant processes in any methodology for creating a statistical learning model. Generally, existing algorithms establish some criterion to select the most influential variables, discarding those that do not contribute any relevant information to the model. This methodology makes sense in a classical static situation where the joint distribution of the data does not vary over time. However, when dealing with real data, it is common to encounter the problem of the dataset shift and, specifically, changes in the relationships between variables (concept shift). In this case, the influence of a variable cannot be the only indicator of its quality as a regressor of the model, since the relationship learned in the traning phase may not correspond to the current situation. Thus, we propose a new feature selection methodology for regression problems that takes this fact into account, using Shapley values to study the effect that each variable has on the predictions. Five examples are analysed: four correspond to typical situations where the method matches the state of the art and one example related to electricity price forecasting where a concept shift phenomenon has occurred in the Iberian market. In this case the proposed algorithm improves the results significantly.

翻译：特征选择是任何统计学习模型构建方法中最关键的过程之一。通常，现有算法会建立某种准则以筛选最具影响力的变量，并剔除那些未向模型贡献相关信息的变量。这种策略在经典静态场景（数据联合分布不随时间变化）下具有合理性。然而在处理真实数据时，常会遇到数据集偏移问题，特别是变量间关系的变化（概念漂移）。在此情况下，变量的影响力不能作为其是否优质模型回归变量的唯一指标，因为训练阶段学习到的变量关系可能已不再适用于当前情境。为此，我们提出了一种考虑上述因素的回归问题特征选择新方法——利用Shapley值研究每个变量对预测结果的影响。本文分析了五个案例：其中四个案例对应该方法与现有技术水平匹配的典型情形，另一个案例则涉及伊比利亚市场电力价格预测中出现的概念漂移现象。在概念漂移案例中，所提算法显著提升了预测效果。