  Data annotation is an essential stage in supervised learning. However, the
annotation process is exhaustive and time consuming, specially for large
datasets. Activities of Daily Living (ADL) recognition is an example of systems
that exploit very large raw sensor data readings. In such systems, sensor
readings are collected from activity-monitoring sensors in a 24/7 manner. The
size of the generated dataset is so huge that it is almost impossible for a
human annotator to give a certain label to every single instance in the
dataset. This results in annotation gaps in the input data to the adopting
supervised learning system. The performance of the recognition system is
negatively affected by these gaps. In this work, we propose and investigate
three different paradigms to handle these gaps. In the first paradigm, the gaps
are taken out by dropping all unlabeled readings. A single "Unknown" or
"Do-Nothing" label is given to the unlabeled readings within the operation of
the second paradigm. The last paradigm handles these gaps by giving every one
of them a unique label identifying the encapsulating deterministic labels.
Also, we propose a semantic preprocessing method of annotation gaps by
constructing a hybrid combination of some of these paradigms for further
performance improvement. The performance of the proposed three paradigms and
their hybrid combination is evaluated using an ADL benchmark dataset containing
more than $2.5\times 10^6$ sensor readings that had been collected over more
than nine months. The evaluation results emphasize the performance contrast
under the operation of each paradigm and support a specific gap handling
approach for better performance.
