Ensemble of Fuzzy Decision Tree for Efficient Indoor Space Recognition

  • Received : 2017.02.08
  • Accepted : 2017.04.10
  • Published : 2017.04.28


In this paper, we expand the process of classification to an ensemble of fuzzy decision tree. For indoor space recognition, many research use Boosted Tree, consists of Adaboost and decision tree. The Boosted Tree extracts an optimal decision tree in stages. On each stage, Boosted Tree extracts the good decision tree by minimizing the weighted error of classification. This decision tree performs a hard decision. In most case, hard decision offer some error when they classify nearby a dividing point. Therefore, We suggest an ensemble of fuzzy decision tree, which offer some flexibility to the Boosted Tree algorithm as well as a high performance. In experimental results, we evaluate that the accuracy of suggested methods improved about 13% than the traditional one.


Supported by : Korea Small and Medium Business Administration


  1. H. Jung, Y. Choi and S. Lee, "Changes in the Fourth Industrial Revolution and Health Industry Paradigm", KHIDI Brief, Vol. 215, May 2016.
  2. F. Meneguzzi, B. Kannan, K. sycara, C. Gnegy, E. Glasgow, P. Yordanov and B. Dias, "Predictive indoor navigation using commercial smart-phones," Proceedings of the 28th Annual ACM Symposium on Applied Computing ACM, March 2013.
  3. S. Siltanen, "Diminished reality for augmented reality interior design," The Visual Computer, pp. 1-16, November 2015.
  4. D. Hoiem, A Efros and M. Hebert, "Recovering surface layout from an image," International Journal of Computer Vision, Vol. 75, No. 1, pp. 151-172, February 2007.
  5. D. Lee, M. Hebert and T. Kanade, "Geometric reasoning for single image structure recovery," Computer Vision and Pattern Recognition, June 2009.
  6. A. Gupta, M. Hebert, T. Kanade and D. Blei, "Estimating spatial layout of rooms using volumetric reasoning about objects and surfaces," Advances in neural information processing systems, December 2010.
  7. S. Ramalingam, J. Pillai, A. Jain and Y. Taguchi, "Manhattan junction catalogue for spatial reasoning of indoor scenes," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, December 2013.
  8. G. Ratsch, T. Onoda and K. Muller, "Soft margins for AdaBoost," Machine learning, Vol. 42, No. 3, pp. 287-320, March 2001.