JSAP Journals

JJAP Conference Proceedings

JJAP Conf. Proc. 4, 011610 (2016) doi:10.7567/JJAPCP.4.011610

Fuzzy classifier hyper-matrices for rapid data classification

Balázs Tusor1,2, Annamária R. Várkonyi-Kóczy1,3,4

  1. 1Integrated Intelligent Systems Japanese-Hungarian Laboratory, Óbuda University, Budapest, Hungary
  2. 2Doctoral School of Applied Informatics, Óbuda University, Budapest, Hungary
  3. 3Institute of Mechatronics and Vehicle Engineering, Óbuda University, Budapest, Hungary
  4. 4Department of Mathematics and Informatics, J. Selye University, Komarno, Slovakia
  • Received September 28, 2015
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In this paper, a novel fuzzy classification method is presented for very fast evaluation. The idea is the usage of a multidimensional matrix for data classification purposes, in which the attribute values of the input data are used as matrix coordinates. The matrices contain the fuzzy membership function values (i.e., the degree of the matrix elements belonging to each class) mapped to integer coordinates in the whole problem domain. This method is ideal for problems where the input values are integer and the quantity of the possible values they can take is finite. A training algorithm is proposed for tuning the classifier. The performance of the system is shown through an experiment.

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