Article ID Journal Published Year Pages File Type
6863922 Neurocomputing 2018 9 Pages PDF
Abstract
This paper presents a framework for dealing with the problem of GPS trajectory classification in the context of the Rio de Janeiro's public transit system (with hundreds or more classes). Such framework combines the versatile WiSARD classifier with a set of rules defined a priori, resulting in a neuro-symbolic learning system with very interesting characteristics and cutting-edge performance. We also verified the influence of different binarization methods in order to adapt raw data to WiSARD, which feeds from binary data only. These ideas were tested against a large data set of trajectories of buses from the city of Rio de Janeiro. The results confirm the practical applicability of those, since the accomplished performance was as good as that of other state-of-the-art rival methods in most test scenarios.
Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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