2017 ANALYTiCAnActiveLearningSystemf

• (Junior et al., 2017) ⇒ Amilcar Soares Junior, Chiara Renso, and Stan Matwin. (2017). “ANALYTiC: An Active Learning System for Trajectory Classification.” In: IEEE Computer Graphics and Applications Journal, 37(5). doi:10.1109/MCG.2017.3621221

Subject Headings: Active Learning System, ANALYTiC.

Cited By

• http://scholar.google.com/scholar?q=%222017%22+ANALYTiC%3A+An+Active+Learning+System+for+Trajectory+Classification

Quotes

Author Keywords

• Computer Graphics; Geographic Data Science; Active Learning; Semantic Annotation; Trajectory Classification; ANALYTiC Platform

Abstract

There is an increasing amount of trajectories data becoming available by the tracking of various moving objects, like animals, vessels, vehicles and humans. However, these large collections of movement data lack semantic annotations, since they are typically done by domain experts in a time consuming activity. A promising approach is the use of machine learning algorithms to try to infer semantic annotations from the trajectories by learning from sets of labeled data. This paper experiments active learning, a machine learning approach minimizing the set of trajectories to be annotated while preserving good performance measures. We test some active learning strategies with three different trajectories datasets with the objective of evaluating how this technique may limit the human effort required for the learning task. We support the annotation task by providing the ANALYTiC platform, a web-based interactive tool to visually assist the user in the active learning process over trajectory data.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2017 ANALYTiCAnActiveLearningSystemf	Stan Matwin Chiara Renso Amilcar Soares Junior			ANALYTiC: An Active Learning System for Trajectory Classification				10.1109/MCG.2017.3621221		2017