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 -