GrNFS: A granular neuro-fuzzy system for regression in large volume data

• Autorzy: Siminski Krzysztof

Identyfikatory

DOI

10.34768/amcs-2021-0030

• Języki publikacji: EN

Abstrakty

EN

Neuro-fuzzy systems have proved their ability to elaborate intelligible nonlinear models for presented data. However, their bottleneck is the volume of data. They have to read all data in order to produce a model. We apply the granular approach and propose a granular neuro-fuzzy system for large volume data. In our method the data are read by parts and granulated. In the next stage the fuzzy model is produced not on data but on granules. In the paper we introduce a novel type of granules: a fuzzy rule. In our system granules are represented by both regular data items and fuzzy rules. Fuzzy rules are a kind of data summaries. The experiments show that the proposed granular neuro-fuzzy system can produce intelligible models even for large volume datasets. The system outperforms the sampling techniques for large volume datasets.

Słowa kluczowe

EN

granular computing; neuro-fuzzy system; large volume data; machine learning

PL

przetwarzanie ziarniste; system neurorozmyty; uczenie maszynowe

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2021
• Tom: Vol. 31, no. 3
• Strony: 445--459
• Opis fizyczny: Bibliogr. 58 poz., rys., tab., wykr.

Twórcy

autor

Siminski Krzysztof

• Department of Algorithmics and Software, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).