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 dealingwith 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.
CITATION STYLE
Trawiński, K., Alonso, J. M., & Cordón, O. (2015). Applying random linear oracles with fuzzy classifier ensembles onWiFi indoor localization problem. Studies in Fuzziness and Soft Computing, 322, 277–287. https://doi.org/10.1007/978-3-319-16235-5_22
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