Filter method-based feature selection process for unattributed-identity multi-target regression problem

Unattributed-identity multi-target regression (UIMTR) is defined as a multi-target regression problem in which the identity of the target and predictor variables is not predefined. It is a problem that can be found in several real-world applications. For example, when historical data is available from a set of devices, but real-time data can only be requested from a subset of them (so called sentinels). For estimating real-time status of non-sentinels, it will be necessary to generate multi-target regression models. Therefore, attributing the identity of the real-time communicators (sentinels), i.e., the predictor variables, is a critical aspect. Moreover, unlike classical feature selection problems, the set of target variables is determined after applying the selection methods and not before, thus, some adaptations are necessary. We introduce three novel methods to solve the UIMTR and, after extensive evaluation, we demonstrate: (i) the feasibility of the methods, (ii) the usefulness of the approach, and (iii) the improvement over other classical techniques. The results have been evaluated from three perspectives: (i) the quality of the predictions, (ii) the stability of the methods and (iii) the execution time.