Abstract
People localization is required for many novel applications such as proactive caring for the elders or people suffering degenerative dementia. In a previous contribution, we introduced a system for people localization in indoor environments based on a topology-based WiFi signal strength fingerprint approach. The well-known curse of dimensionality critically emerges when dealing with these kinds of complex environments. We address the localization task as a high dimensional classification problem that can only be effectively addressed by an advanced classifier ensemble approach. Therefore, in this paper we present a localization system based on a fuzzy rule-based classifier ensemble framework where we consider a random linear oracle for the component classifier generation, as this fast and generic method induces more diversity thus improving the final performance. The proposed system is validated in a real environment, achieving very promising results. Its ability to handle the huge uncertainty that is characteristic of WiFi signals is demonstrated.
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Abbreviations
- AP :
-
Access Point
- Bag :
-
Bagging
- CE :
-
Classifier Ensemble
- DP :
-
Decision Profile
- FRBCE :
-
Fuzzy Rule-Based Classifier Ensemble
- FURIA :
-
Fuzzy Unordered Rule Induction Algorithm
- RLO :
-
Random Linear Oracle
- RS :
-
Random Subspace
- RSS :
-
Received Signal Strength
- SC :
-
Soft Computing
- UAH :
-
University of Alcalá
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Acknowledgments
The authors would like to acknowledge the strong and positive influence Prof. Enric Trillas has played for the development of the fuzzy sets and systems research area in Spain and worldwide. They are very proud of having had the chance to meet Enric and collaborate with him along his long and productive scientific career. In particular, the authors are especially glad of having taken part in his last but definitively not less important achievement, that is the creation of the European Centre for Soft Computing, where they three are or have been enrolled.
This work has been supported by the Spanish Ministerio de Economía y Competitividad under the ABSYNTHE (TIN2011-29824-C02-01 and TIN2011-29824-C02-02) and the SOCOVIFI2 (TIN2012-38525-C01) projects.
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Trawiński, K., Alonso, J.M., Cordón, O. (2015). Applying Random Linear Oracles with Fuzzy Classifier Ensembles on WiFi Indoor Localization Problem. In: Magdalena, L., Verdegay, J., Esteva, F. (eds) Enric Trillas: A Passion for Fuzzy Sets. Studies in Fuzziness and Soft Computing, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-319-16235-5_22
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