  In traditional Machine Learning, the algorithms predictions are based on the
assumption that the data follows the same distribution in both the training and
the test datasets. However, in real world data this condition does not hold
and, for instance, the distribution of the covariates changes whereas the
conditional distribution of the targets remains unchanged. This situation is
called covariate shift problem where standard error estimation may be no longer
accurate. In this context, the importance is a measure commonly used to
alleviate the influence of covariate shift on error estimations. The main
drawback is that it is not easy to compute. The Kullback-Leibler Importance
Estimation Procedure (KLIEP) is capable of estimating importance in a promising
way. Despite its good performance, it fails to ignore target information, since
it only includes the covariates information for computing the importance. In
this direction, this paper explores the potential performance improvement if
target information is considered in the computation of the importance. Then, a
redefinition of the importance arises in order to be generalized in this way.
Besides the potential improvement in performance, including target information
make possible the application to a real application about plankton
classification that motivates this research and characterized by its great
dimensionality, since considering targets rather than covariates reduces the
computation and the noise in the covariates. The impact of taking target
information is also explored when Logistic Regression (LR), Kernel Mean
Matching (KMM), Ensemble Kernel Mean Matching (EKMM) and the naive predecessor
of KLIEP called Kernel Density Estimation (KDE) methods estimate the
importance. The experimental results lead to a more accurate error estimation
using target information, especially in case of the more promising method
KLIEP.
