Applying Random Linear Oracles with Fuzzy Classifier Ensembles on WiFi Indoor Localization Problem

  • Krzysztof TrawińskiEmail author
  • Jose M. Alonso
  • Oscar Cordón
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 322)


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.


WiFi localization Classifier ensembles Bagging Random subspace Random linear oracles Fuzzy rule-based classifier ensembles 



Access Point




Classifier Ensemble


Decision Profile


Fuzzy Rule-Based Classifier Ensemble


Fuzzy Unordered Rule Induction Algorithm


Random Linear Oracle


Random Subspace


Received Signal Strength


Soft Computing


University of Alcalá



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.


  1. 1.
    Alonso, J.M., Ocaña, M., Hernández, N., Herranz, F., Llamazares, Á., Sotelo, M.Á., Bergasa, L.M., Magdalena, L.: Enhanced WiFi localization system based on soft computing techniques to deal with small-scale variations in wireless sensors. Appl. Soft Comput. 11(8), 4677–4691 (2011)CrossRefGoogle Scholar
  2. 2.
    Bahillo, A., Lorenzo, R.M., Mazuelas, S., Fernández, P., Abril, E.J.: Assessment of the shadow caused by the human body on the personal RF dosimeters reading in multipath environments. In: Biomedical Engineering, pp. 133–144. InTech (2009)Google Scholar
  3. 3.
    Banfield, R.E., Hall, L.O., Bowyer, K.W., Kegelmeyer, W.P.: A comparison of decision tree ensemble creation techniques. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 173–180 (2007)CrossRefGoogle Scholar
  4. 4.
    Bonissone, P.P., Cadenas, J.M., Garrido, M.C., Díaz-Valladares, R.A.: A fuzzy random forest. Int. J. Approx. Reason. 51(7), 729–747 (2010)CrossRefGoogle Scholar
  5. 5.
    Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)zbMATHMathSciNetGoogle Scholar
  6. 6.
    Dasarathy, B.V., Sheela, B.V.: A composite classifier system design: concepts and methodology. Proc. IEEE 67(5), 708–713 (1979)CrossRefGoogle Scholar
  7. 7.
    Elnahrawy, E., Li, X., Martin, R.P.: The limits of localization using signal strength: a comparative study. In: First Annual IEEE Communications Society Conference on Sensor Ad Hoc Communications and Networks, pp. 406–414 (2004)Google Scholar
  8. 8.
    García-Valverde, T., García-Sola, A., Hagras, H., Dooley, J., Callaghan, V., Botía, J.A.: A fuzzy logic-based system for indoor localization using WiFi in ambient intelligent environments. IEEE Trans. Fuzzy Syst. 21(4), 702–718 (2013)CrossRefGoogle Scholar
  9. 9.
    Hernández, N., Alonso, J.M., Magro, M., Ocaña, M.: Hierarchical WiFi localization system. In: International Workshop on Perception in Robotics, IEEE Intelligent Vehicles Symposium, pp. P21.1–P21.6 (2012)Google Scholar
  10. 10.
    Ho, T.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)CrossRefGoogle Scholar
  11. 11.
    Hühn, J.C., Hüllermeier, E.: FURIA: An algorithm for unordered fuzzy rule induction. Data Min. Knowl. Discov. 19(3), 293–319 (2009)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Kuncheva, L.I., Rodríguez, J.J.: Classifier ensembles with a random linear oracle. IEEE Trans. Knowl. Data Eng. 19(4), 500–508 (2007)CrossRefGoogle Scholar
  13. 13.
    Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, New Jersey (2004)CrossRefGoogle Scholar
  14. 14.
    Kuncheva, L.I.: A theoretical study on six classifier fusion strategies. IEEE Trans. Pattern Anal. Mach. Intell. 24, 281–286 (2002)CrossRefGoogle Scholar
  15. 15.
    Magdalena, L.: What is soft computing? Revisiting possible answers. Int. J. Comput. Intell. Syst. 3(2), 148–159 (2010)CrossRefGoogle Scholar
  16. 16.
    Matellán, V., Cañas, J.M., Serrano, O.: WiFi localization methods for autonomous robots. Robotica 24(4), 455–461 (2006)CrossRefGoogle Scholar
  17. 17.
    Menéndez, P., Campomanes, C., Trawiński, K., Alonso, J.M.: Topology-based indoor localization by means of WiFi fingerprinting with a computational intelligent classifier. In: Proceedings of the 11th IEEE International Conference on Intelligent System Design and Applications, pp. 1020–1025 (2011)Google Scholar
  18. 18.
    Nerguizian, C., Belkhous, S., Azzouz, A., Nerguizian, V., Saad, M.: Mobile robot geolocation with received signal strength (RSS) fingerprinting technique and neural networks. In: IEEE International Conference on Industrial Technology, pp. 1183–1185 (2004)Google Scholar
  19. 19.
    Optiz, D., Maclin, R.: Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 11, 169–198 (1999)Google Scholar
  20. 20.
    Palmer, N., Kemp, R., Kielmann, T., Bal, H.: The case for smartphones as an urgent computing client platform. Procedia Comput. Sci. 9, 1667–1676 (2012)CrossRefGoogle Scholar
  21. 21.
    Panov, P., Džeroski, S.: Combining bagging and random subspaces to create better ensembles. In: Proceedings of the 7th International Conference on Intelligent Data Analysis, pp. 118–129. Springer (2007)Google Scholar
  22. 22.
    Schapire, R.: The strength of weak learnability. Mach. Learn. 5(2), 197–227 (1990)Google Scholar
  23. 23.
    Trawiński, K., Alonso, J.M., Hernández, N.: A multiclassifier approach for topology-based WiFi indoor localization. Soft Comput. 17(10), 1817–1831 (2013)CrossRefGoogle Scholar
  24. 24.
    Trawiński, K., Cordón, O., Quirin, A.: On designing fuzzy rule-based multiclassification systems by combining FURIA with bagging and feature selection. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 19(4), 589–633 (2011)CrossRefGoogle Scholar
  25. 25.
    Tsymbal, A., Pechenizkiy, M., Cunningham, P.: Diversity in search strategies for ensemble feature selection. Inf. Fusion 6(1), 83–98 (2005)CrossRefGoogle Scholar
  26. 26.
    Woods, K., Kegelmeyer, W.P., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Trans. Pattern Anal. Mach. Intell. 19, 405–410 (1997)CrossRefGoogle Scholar
  27. 27.
    Zhou, Z.H.: Ensembling local learners through multimodal perturbation. IEEE Trans. Syst., Man, Cybern., Part B: Cybern. 35(4), 725–735 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Krzysztof Trawiński
    • 1
    Email author
  • Jose M. Alonso
    • 1
  • Oscar Cordón
    • 1
    • 2
  1. 1.European Centre for Soft ComputingAsturiasSpain
  2. 2.Department of Computer Science and Artificial Intelligence (DECSAI) and Research Center on Information and Communication Technologies (CITIC-UGR)University of GranadaGranadaSpain

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