  Algorithm Selection (AS) is concerned with the selection of the best-suited
algorithm out of a set of candidates for a given problem. The area of AS has
received a lot of attention from machine learning researchers and
practitioners, as positive results along this line of research can make
expertise in ML more readily accessible to experts in other domains as well as
to the general public. Another quickly expanding area is that of Multi-Target
Prediction (MTP). The ability to simultaneously predict multiple target
variables of diverse types makes MTP of interest for a plethora of
applications. MTP embraces several subfields of machine learning, such as
multi-label classification, multi-target regression, multi-task learning,
dyadic prediction, zero-shot learning, network inference, and matrix
completion. This work combines the two above-mentioned areas by proposing
AutoMTP, an automated framework that performs algorithm selection for MTP.
AutoMTP is realized by adopting a rule-based system for the algorithm selection
step and a flexible neural network architecture that can be used for the
several subfields of MTP.
