| dc.description.abstract | This paper describes a study into the classification of
gender based on viewing behavior. This was done with the
data of 1242 visitors of the NEMO museum, to which we
had to pick a classification algorithm and decide on what
features to use with this algorithm to train and test our
given data with. We evaluated the algorithm based on
multiple machine learning measures, such as Precision,
Recall and F1-score, but the most important measure,
which was also the measure we were basing our
evaluation on, was the Accuracy measure. Our criteria for
a good algorithm was set to 70%, which was based on
related work. Our algorithm with the implemented feature
set got exactly that as Accuracy, to which we can
conclude that it is indeed possible to program an
algorithm that can correctly classify sex based on eye
tracking data. This has a few implications: by further
analysing eye tracking data and successfully furthering
algorithms to also correctly classify variables such as age
and mood of a person, we can predict the way people are
going to behave and make things such as advertisements
more effective. | |
