Topology-based classification error calculation based on indoorGML document

Ilku, Krisztián and Tamás, Judit: Topology-based classification error calculation based on indoorGML document. Conference of PhD Students in Computer Science, (11). pp. 101-105. (2018)

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Topology-based classification error calculation method for symbolic indoor positioning is presented based on IndoorGML document. Symbolic indoor positions or Zones are well-defined parts of the building, which can be treated as a classification category. The evaluation of well-known classifiers is based on the classical CRISP approach, which considers each misclassification equally wrong. Our previous experimental results revealed the need to consider the topology in the classification error calculation. A possible solution for this challenge is gravitational force based approach, which calculates the classification error by the size and the layout of the Zones. Testing the criteria against this approach in real-life scenario, real-life environment is required. IndoorGML is a standard for specifying indoor spatial information, and it represents the indoor space as non-overlapping closed objects. These indoor spaces are bounded by physical or fictional boundaries, and the representation of an object is by both geometric shape and bounding box. Thus, IndoorGML standard can be used both for modeling the indoor environment and calculation the classification error for symbolic indoor positioning services. In this paper, the gravitational force based approach is examined in real-life environment of Institute of Information Science building in University of Miskolc defined in IndoorGML Document.

Item Type: Article
Event Title: Conference of PhD students in computer science (11.) (2018) (Szeged)
Journal or Publication Title: Conference of PhD Students in Computer Science
Date: 2018
Volume: 11
Page Range: pp. 101-105
Uncontrolled Keywords: Számítástechnika
Additional Information: Bibliogr.: p. 104-105. ; összefoglalás angol nyelven
Date Deposited: 2019. Nov. 04. 10:28
Last Modified: 2019. Nov. 04. 10:28

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