Modular Rough Neuro-fuzzy Systems for Classification (bibtex)
by Rafał Scherer, Marcin Korytkowski, Robert Nowicki, Leszek Rutkowski
Abstract:
In the paper we propose a new class of modular systems for classification in the case of missing features. We incorporate the rough set theory into construction of neuro-fuzzy systems which create the modular structure. The AdaBoost algorithm is combined with the gradient algorithm to train the whole system. We illustrate the performance of our approach on typical benchmarks.
Reference:
R. Scherer, M. Korytkowski, R. Nowicki, L. Rutkowski, "Modular Rough Neuro-fuzzy Systems for Classification", Lecture Notes in Computer Science, vol. 4967, 2008, pp. 540-548.
Bibtex Entry:
@ARTICLE{SchKorNowRut_PPAM2008,
  author = {Rafał Scherer and Marcin Korytkowski and Robert Nowicki and Leszek
	Rutkowski},
  title = {Modular Rough Neuro-fuzzy Systems for Classification},
  journal = {Lecture Notes in Computer Science},
  year = {2008},
  volume = {4967},
  pages = {540-548},
  abstract = {In the paper we propose a new class of modular systems for classification
	in the case of missing features. We incorporate the rough set theory
	into construction of neuro-fuzzy systems which create the modular
	structure. The AdaBoost algorithm is combined with the gradient algorithm
	to train the whole system. We illustrate the performance of our approach
	on typical benchmarks.},
  url = {http://www.springerlink.com/content/372n3787p08x817m/}
}
Powered by bibtexbrowser