Design of Polynomial Fuzzy Neural Network Classifiers Based on Density Fuzzy C-Means and L2-Norm Regularization

Shaocong Xue; Wei Huang; Chuanyin Yang; Jinsong Wang

doi:10.1007/978-981-15-0118-0_45

Design of Polynomial Fuzzy Neural Network Classifiers Based on Density Fuzzy C-Means and L2-Norm Regularization

Shaocong Xue, Wei Huang^*, Chuanyin Yang, Jinsong Wang

^*Corresponding author for this work

Tianjin University of Technology

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

Abstract

In this paper, polynomial fuzzy neural network classifiers (PFNNCs) is proposed by means of density fuzzy c-means and L2-norm regularization. The overall design of PFNNCs was realized by means of fuzzy rules that come in form of three parts, namely premise part, consequence part and aggregation part. The premise part was developed by density fuzzy c-means that helps determine the apex parameters of membership functions, while the consequence part was realized by means of two types of polynomials including linear and quadratic. L2-norm regularization that can alleviate the overfitting problem was exploited to estimate the parameters of polynomials, which constructed the aggregation part. Experimental results of several data sets demonstrate that the proposed classifiers show higher classification accuracy in comparison with some other classifiers reported in the literature.

Original language	English
Title of host publication	Data Science - 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019, Proceedings
Editors	Xiaohui Cheng, Weipeng Jing, Xianhua Song, Zeguang Lu
Publisher	Springer Verlag
Pages	585-596
Number of pages	12
ISBN (Print)	9789811501173
DOIs	https://doi.org/10.1007/978-981-15-0118-0_45
Publication status	Published - 2019
Externally published	Yes
Event	5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019 - Guilin, China Duration: 20 Sept 2019 → 23 Sept 2019

Publication series

Name	Communications in Computer and Information Science
Volume	1058
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019
Country/Territory	China
City	Guilin
Period	20/09/19 → 23/09/19

Keywords

Density fuzzy clustering
Fuzzy rules
L2-norm regularization
Polynomial fuzzy neural network classifiers

Access to Document

10.1007/978-981-15-0118-0_45

Cite this

Xue, S., Huang, W., Yang, C., & Wang, J. (2019). Design of Polynomial Fuzzy Neural Network Classifiers Based on Density Fuzzy C-Means and L2-Norm Regularization. In X. Cheng, W. Jing, X. Song, & Z. Lu (Eds.), Data Science - 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019, Proceedings (pp. 585-596). (Communications in Computer and Information Science; Vol. 1058). Springer Verlag. https://doi.org/10.1007/978-981-15-0118-0_45

Xue, Shaocong ; Huang, Wei ; Yang, Chuanyin et al. / Design of Polynomial Fuzzy Neural Network Classifiers Based on Density Fuzzy C-Means and L2-Norm Regularization. Data Science - 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019, Proceedings. editor / Xiaohui Cheng ; Weipeng Jing ; Xianhua Song ; Zeguang Lu. Springer Verlag, 2019. pp. 585-596 (Communications in Computer and Information Science).

@inproceedings{31ecb1fd6ab14cba93129dc9a81bd8ba,

title = "Design of Polynomial Fuzzy Neural Network Classifiers Based on Density Fuzzy C-Means and L2-Norm Regularization",

abstract = "In this paper, polynomial fuzzy neural network classifiers (PFNNCs) is proposed by means of density fuzzy c-means and L2-norm regularization. The overall design of PFNNCs was realized by means of fuzzy rules that come in form of three parts, namely premise part, consequence part and aggregation part. The premise part was developed by density fuzzy c-means that helps determine the apex parameters of membership functions, while the consequence part was realized by means of two types of polynomials including linear and quadratic. L2-norm regularization that can alleviate the overfitting problem was exploited to estimate the parameters of polynomials, which constructed the aggregation part. Experimental results of several data sets demonstrate that the proposed classifiers show higher classification accuracy in comparison with some other classifiers reported in the literature.",

keywords = "Density fuzzy clustering, Fuzzy rules, L2-norm regularization, Polynomial fuzzy neural network classifiers",

author = "Shaocong Xue and Wei Huang and Chuanyin Yang and Jinsong Wang",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.; 5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019 ; Conference date: 20-09-2019 Through 23-09-2019",

year = "2019",

doi = "10.1007/978-981-15-0118-0\_45",

language = "English",

isbn = "9789811501173",

series = "Communications in Computer and Information Science",

publisher = "Springer Verlag",

pages = "585--596",

editor = "Xiaohui Cheng and Weipeng Jing and Xianhua Song and Zeguang Lu",

booktitle = "Data Science - 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019, Proceedings",

address = "Germany",

}

Xue, S, Huang, W, Yang, C & Wang, J 2019, Design of Polynomial Fuzzy Neural Network Classifiers Based on Density Fuzzy C-Means and L2-Norm Regularization. in X Cheng, W Jing, X Song & Z Lu (eds), Data Science - 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019, Proceedings. Communications in Computer and Information Science, vol. 1058, Springer Verlag, pp. 585-596, 5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019, Guilin, China, 20/09/19. https://doi.org/10.1007/978-981-15-0118-0_45

Design of Polynomial Fuzzy Neural Network Classifiers Based on Density Fuzzy C-Means and L2-Norm Regularization. / Xue, Shaocong; Huang, Wei; Yang, Chuanyin et al.
Data Science - 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019, Proceedings. ed. / Xiaohui Cheng; Weipeng Jing; Xianhua Song; Zeguang Lu. Springer Verlag, 2019. p. 585-596 (Communications in Computer and Information Science; Vol. 1058).

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

TY - GEN

T1 - Design of Polynomial Fuzzy Neural Network Classifiers Based on Density Fuzzy C-Means and L2-Norm Regularization

AU - Xue, Shaocong

AU - Huang, Wei

AU - Yang, Chuanyin

AU - Wang, Jinsong

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2019.

PY - 2019

Y1 - 2019

N2 - In this paper, polynomial fuzzy neural network classifiers (PFNNCs) is proposed by means of density fuzzy c-means and L2-norm regularization. The overall design of PFNNCs was realized by means of fuzzy rules that come in form of three parts, namely premise part, consequence part and aggregation part. The premise part was developed by density fuzzy c-means that helps determine the apex parameters of membership functions, while the consequence part was realized by means of two types of polynomials including linear and quadratic. L2-norm regularization that can alleviate the overfitting problem was exploited to estimate the parameters of polynomials, which constructed the aggregation part. Experimental results of several data sets demonstrate that the proposed classifiers show higher classification accuracy in comparison with some other classifiers reported in the literature.

AB - In this paper, polynomial fuzzy neural network classifiers (PFNNCs) is proposed by means of density fuzzy c-means and L2-norm regularization. The overall design of PFNNCs was realized by means of fuzzy rules that come in form of three parts, namely premise part, consequence part and aggregation part. The premise part was developed by density fuzzy c-means that helps determine the apex parameters of membership functions, while the consequence part was realized by means of two types of polynomials including linear and quadratic. L2-norm regularization that can alleviate the overfitting problem was exploited to estimate the parameters of polynomials, which constructed the aggregation part. Experimental results of several data sets demonstrate that the proposed classifiers show higher classification accuracy in comparison with some other classifiers reported in the literature.

KW - Density fuzzy clustering

KW - Fuzzy rules

KW - L2-norm regularization

KW - Polynomial fuzzy neural network classifiers

UR - http://www.scopus.com/inward/record.url?scp=85072981146&partnerID=8YFLogxK

U2 - 10.1007/978-981-15-0118-0_45

DO - 10.1007/978-981-15-0118-0_45

M3 - Conference contribution

AN - SCOPUS:85072981146

SN - 9789811501173

T3 - Communications in Computer and Information Science

SP - 585

EP - 596

BT - Data Science - 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019, Proceedings

A2 - Cheng, Xiaohui

A2 - Jing, Weipeng

A2 - Song, Xianhua

A2 - Lu, Zeguang

PB - Springer Verlag

T2 - 5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019

Y2 - 20 September 2019 through 23 September 2019

ER -

Xue S, Huang W, Yang C, Wang J. Design of Polynomial Fuzzy Neural Network Classifiers Based on Density Fuzzy C-Means and L2-Norm Regularization. In Cheng X, Jing W, Song X, Lu Z, editors, Data Science - 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019, Proceedings. Springer Verlag. 2019. p. 585-596. (Communications in Computer and Information Science). doi: 10.1007/978-981-15-0118-0_45

Design of Polynomial Fuzzy Neural Network Classifiers Based on Density Fuzzy C-Means and L2-Norm Regularization

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this