Fig. 3: Generating decision trees from acceleration data.
From: Machine learning enables improved runtime and precision for bio-loggers on seabirds
a We start by converting the raw three-axis data (row one) into magnitude of acceleration values (row two) and segmenting the data into 1-s windows. We then extract the ACC features listed in Supplementary Table 1 from each window. Rows three and four show examples of the features extracted, which can be used to differentiate between the behaviours. For example, Crest can be used to identify Flying behaviour, since its values are higher for Flying than for the other two behaviours. b An example decision tree generated from the feature values shown in the lower two rows of (a), with each leaf (grey) node representing a final predicted class for a 1-s segment of data. Supplementary Data 1 provides source data of this figure.
