<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

International Journal of Applied Mathematics and Computer Science

{"article-title":"Neuro-rough-fuzzy approach for regression modelling from missing data"}

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-rough-fuzzy approach for regression modelling from missing data

Online veröffentlicht: 28. Juni 2012

Seitenbereich: 461 - 476

DOI: https://doi.org/10.2478/v10006-012-0035-4

Schlüsselwörter
neuro-fuzzy, ANNBFIS, missing values, marginalisation, imputation, rough fuzzy set, clustering

This content is open access.

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

Online veröffentlicht: 28. Juni 2012

Seitenbereich: 461 - 476

DOI: https://doi.org/10.2478/v10006-012-0035-4

Schlüsselwörterneuro-fuzzy, ANNBFIS, missing values, marginalisation, imputation, rough fuzzy set, clustering

This content is open access.

Schlüsselwörter
neuro-fuzzy, ANNBFIS, missing values, marginalisation, imputation, rough fuzzy set, clustering