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

International Journal of Applied Mathematics and Computer Science (2012)

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

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topKrzysztof 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 -

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