Neuro-rough-fuzzy approach for regression modelling from missing data

Krzysztof Simiński

International Journal of Applied Mathematics and Computer Science (2012)

  • Volume: 22, Issue: 2, page 461-476
  • ISSN: 1641-876X

Abstract

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Real life data sets often suffer from missing data. The neuro-rough-fuzzy systems proposed hitherto often cannot handle such situations. The paper presents a neuro-fuzzy system for data sets with missing values. The proposed solution is a complete neuro-fuzzy system. The system creates a rough fuzzy model from presented data (both full and with missing values) and is able to elaborate the answer for full and missing data examples. The paper also describes the dedicated clustering algorithm. The paper is accompanied by results of numerical experiments.

How to cite

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Krzysztof Simiński. "Neuro-rough-fuzzy approach for regression modelling from missing data." International Journal of Applied Mathematics and Computer Science 22.2 (2012): 461-476. <http://eudml.org/doc/208122>.

@article{KrzysztofSimiński2012,
abstract = {Real life data sets often suffer from missing data. The neuro-rough-fuzzy systems proposed hitherto often cannot handle such situations. The paper presents a neuro-fuzzy system for data sets with missing values. The proposed solution is a complete neuro-fuzzy system. The system creates a rough fuzzy model from presented data (both full and with missing values) and is able to elaborate the answer for full and missing data examples. The paper also describes the dedicated clustering algorithm. The paper is accompanied by results of numerical experiments.},
author = {Krzysztof Simiński},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {neuro-fuzzy; ANNBFIS; missing values; marginalisation; imputation; rough fuzzy set; clustering; neuro-fuzzy system},
language = {eng},
number = {2},
pages = {461-476},
title = {Neuro-rough-fuzzy approach for regression modelling from missing data},
url = {http://eudml.org/doc/208122},
volume = {22},
year = {2012},
}

TY - JOUR
AU - Krzysztof Simiński
TI - Neuro-rough-fuzzy approach for regression modelling from missing data
JO - International Journal of Applied Mathematics and Computer Science
PY - 2012
VL - 22
IS - 2
SP - 461
EP - 476
AB - Real life data sets often suffer from missing data. The neuro-rough-fuzzy systems proposed hitherto often cannot handle such situations. The paper presents a neuro-fuzzy system for data sets with missing values. The proposed solution is a complete neuro-fuzzy system. The system creates a rough fuzzy model from presented data (both full and with missing values) and is able to elaborate the answer for full and missing data examples. The paper also describes the dedicated clustering algorithm. The paper is accompanied by results of numerical experiments.
LA - eng
KW - neuro-fuzzy; ANNBFIS; missing values; marginalisation; imputation; rough fuzzy set; clustering; neuro-fuzzy system
UR - http://eudml.org/doc/208122
ER -

References

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