  Internet of Things (IoT) devices generate heterogeneous data over time; and
relying solely on individual data points is inadequate for accurate analysis.
  Segmentation is a common preprocessing step in many IoT applications,
including IoT-based activity recognition, aiming to address the limitations of
individual events and streamline the process. However, this step introduces at
least two families of uncontrollable biases. The first is caused by the changes
made by the segmentation process on the initial problem space, such as dividing
the input data into 60 seconds windows. The second category of biases results
from the segmentation process itself, including the fixation of the
segmentation method and its parameters.
  To address these biases, we propose to redefine the segmentation problem as a
special case of a decomposition problem, including three key components: a
decomposer, resolutions, and a composer.
  The inclusion of the composer task in the segmentation process facilitates an
assessment of the relationship between the original problem and the problem
after the segmentation. Therefore, It leads to an improvement in the evaluation
process and, consequently, in the selection of the appropriate segmentation
method.
  Then, we formally introduce our novel meta-decomposition or
learning-to-decompose approach. It reduces the segmentation biases by
considering the segmentation as a hyperparameter to be optimized by the outer
learning problem. Therefore, meta-decomposition improves the overall system
performance by dynamically selecting the appropriate segmentation method
without including the mentioned biases. Extensive experiments on four
real-world datasets demonstrate the effectiveness of our proposal.
