Evolving granular neural network for semi-supervised data stream classification

Daniel Leite; Pyramo Costa; Fernando Gomide

doi:10.1109/IJCNN.2010.5596303

Evolving granular neural network for semi-supervised data stream classification

Daniel Leite, Pyramo Costa, Fernando Gomide

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

38 Scopus citations

Abstract

In this paper we introduce an adaptive fuzzy neural network framework for classification of data stream using a partially supervised learning algorithm. The framework consists of an evolving granular neural network capable of processing nonstationary data streams using a one-pass incremental algorithm. The granular neural network evolves fuzzy hyperboxes and uses nullnorm based neurons to classify data. The learning algorithm performs structural and parametric adaptation whenever environment changes are reflected in input data. It needs no prior statistical knowledge about data and classes. Computational experiments show that the fuzzy granular neural network is robust against different types of concept drift, and is able to handle unlabeled examples efficiently.

Original language	English
Title of host publication	2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
DOIs	https://doi.org/10.1109/IJCNN.2010.5596303
State	Published - 2010
Externally published	Yes
Event	2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona, Spain Duration: 18 Jul 2010 → 23 Jul 2010

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
Country/Territory	Spain
City	Barcelona
Period	18/07/10 → 23/07/10

Access to Document

10.1109/IJCNN.2010.5596303

Cite this

Leite, D., Costa, P., & Gomide, F. (2010). Evolving granular neural network for semi-supervised data stream classification. In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 Article 5596303 (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2010.5596303

@inproceedings{cabc85de9a654d42aef08fa6bb379055,

title = "Evolving granular neural network for semi-supervised data stream classification",

abstract = "In this paper we introduce an adaptive fuzzy neural network framework for classification of data stream using a partially supervised learning algorithm. The framework consists of an evolving granular neural network capable of processing nonstationary data streams using a one-pass incremental algorithm. The granular neural network evolves fuzzy hyperboxes and uses nullnorm based neurons to classify data. The learning algorithm performs structural and parametric adaptation whenever environment changes are reflected in input data. It needs no prior statistical knowledge about data and classes. Computational experiments show that the fuzzy granular neural network is robust against different types of concept drift, and is able to handle unlabeled examples efficiently.",

author = "Daniel Leite and Pyramo Costa and Fernando Gomide",

year = "2010",

doi = "10.1109/IJCNN.2010.5596303",

language = "English",

isbn = "9781424469178",

series = "Proceedings of the International Joint Conference on Neural Networks",

booktitle = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010",

note = "2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 ; Conference date: 18-07-2010 Through 23-07-2010",

}

Leite, D, Costa, P & Gomide, F 2010, Evolving granular neural network for semi-supervised data stream classification. in 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010., 5596303, Proceedings of the International Joint Conference on Neural Networks, 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010, Barcelona, Spain, 18/07/10. https://doi.org/10.1109/IJCNN.2010.5596303

Evolving granular neural network for semi-supervised data stream classification. / Leite, Daniel; Costa, Pyramo; Gomide, Fernando.
2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010. 2010. 5596303 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Evolving granular neural network for semi-supervised data stream classification

AU - Leite, Daniel

AU - Costa, Pyramo

AU - Gomide, Fernando

PY - 2010

Y1 - 2010

N2 - In this paper we introduce an adaptive fuzzy neural network framework for classification of data stream using a partially supervised learning algorithm. The framework consists of an evolving granular neural network capable of processing nonstationary data streams using a one-pass incremental algorithm. The granular neural network evolves fuzzy hyperboxes and uses nullnorm based neurons to classify data. The learning algorithm performs structural and parametric adaptation whenever environment changes are reflected in input data. It needs no prior statistical knowledge about data and classes. Computational experiments show that the fuzzy granular neural network is robust against different types of concept drift, and is able to handle unlabeled examples efficiently.

AB - In this paper we introduce an adaptive fuzzy neural network framework for classification of data stream using a partially supervised learning algorithm. The framework consists of an evolving granular neural network capable of processing nonstationary data streams using a one-pass incremental algorithm. The granular neural network evolves fuzzy hyperboxes and uses nullnorm based neurons to classify data. The learning algorithm performs structural and parametric adaptation whenever environment changes are reflected in input data. It needs no prior statistical knowledge about data and classes. Computational experiments show that the fuzzy granular neural network is robust against different types of concept drift, and is able to handle unlabeled examples efficiently.

UR - http://www.scopus.com/inward/record.url?scp=79959389813&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2010.5596303

DO - 10.1109/IJCNN.2010.5596303

M3 - Conference contribution

AN - SCOPUS:79959389813

SN - 9781424469178

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

Y2 - 18 July 2010 through 23 July 2010

ER -

Evolving granular neural network for semi-supervised data stream classification

Abstract

Publication series

Conference

Access to Document

Fingerprint

Cite this