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

International Journal of Applied Mathematics and Computer Science

Volume 22 (2012): Issue 2 (June 2012)

By: Krzysztof Simiński

Open Access

|Jun 2012

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.

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Articles in this issue

DOI: https://doi.org/10.2478/v10006-012-0035-4 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X

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Language: English

Page range: 461 - 476

Published on: Jun 28, 2012

Published by: University of Zielona Góra

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

missing values,

marginalisation,

rough fuzzy set,

Related subjects:

Applied mathematics

© 2012 Krzysztof Simiński, published by University of Zielona Góra
This work is licensed under the Creative Commons License.

Volume 22 (2012): Issue 2 (June 2012)