  In multi-class classification tasks, like human activity recognition, it is
often assumed that classes are separable. In real applications, this assumption
becomes strong and generates inconsistencies. Besides, the most commonly used
approach is to learn classes one-by-one against the others. This computational
simplification principle introduces strong inductive biases on the learned
theories. In fact, the natural connections among some classes, and not others,
deserve to be taken into account. In this paper, we show that the organization
of overlapping classes (multiple inheritances) into hierarchies considerably
improves classification performances. This is particularly true in the case of
activity recognition tasks featured in the SHL dataset. After theoretically
showing the exponential complexity of possible class hierarchies, we propose an
approach based on transfer affinity among the classes to determine an optimal
hierarchy for the learning process. Extensive experiments show improved
performances and a reduction in the number of examples needed to learn.
