On classification with missing data using rough-neuro-fuzzy systems

Robert K. Nowicki

International Journal of Applied Mathematics and Computer Science (2010)

  • Volume: 20, Issue: 1, page 55-67
  • ISSN: 1641-876X

Abstract

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The paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in the case of missing features are described.

How to cite

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Robert K. Nowicki. "On classification with missing data using rough-neuro-fuzzy systems." International Journal of Applied Mathematics and Computer Science 20.1 (2010): 55-67. <http://eudml.org/doc/207977>.

@article{RobertK2010,
abstract = {The paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in the case of missing features are described.},
author = {Robert K. Nowicki},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {fuzzy sets; rough sets; neuro-fuzzy architectures; classification; missing data},
language = {eng},
number = {1},
pages = {55-67},
title = {On classification with missing data using rough-neuro-fuzzy systems},
url = {http://eudml.org/doc/207977},
volume = {20},
year = {2010},
}

TY - JOUR
AU - Robert K. Nowicki
TI - On classification with missing data using rough-neuro-fuzzy systems
JO - International Journal of Applied Mathematics and Computer Science
PY - 2010
VL - 20
IS - 1
SP - 55
EP - 67
AB - The paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in the case of missing features are described.
LA - eng
KW - fuzzy sets; rough sets; neuro-fuzzy architectures; classification; missing data
UR - http://eudml.org/doc/207977
ER -

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