  Identifying phases of flight is important in the field of general aviation,
as knowing which phase of flight data is collected from aircraft flight data
recorders can aid in the more effective detection of safety or hazardous
events. General aviation flight data for phase of flight identification is
usually per-second data, comes on a large scale, and is class imbalanced. It is
expensive to manually label the data and training classification models usually
faces class imbalance problems. This work investigates the use of a novel
method for minimally supervised self-organizing maps (MS-SOMs) which utilize
nearest neighbor majority votes in the SOM U-matrix for class estimation.
Results show that the proposed method can reach or exceed a naive SOM approach
which utilized a full data file of labeled data, with only 30 labeled
datapoints per class. Additionally, the minimally supervised SOM is
significantly more robust to the class imbalance of the phase of flight data.
These results highlight how little data is required for effective phase of
flight identification.
