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