Two-dimensional Gaussian hierarchical priority fuzzy modeling for interval-valued data

被引：10

作者：

Liu, Xiaotian ^{[1
]}

Zhao, Tao ^{[1
]}

Xie, Xiangpeng ^{[1
,2
]}

机构：

[1] Sichuan Univ, Coll Elect Engn, Chengdu 610065, Peoples R China

[2] Nanjing Univ Posts & Telecommun, Inst Adv Technol, Nanjing 210023, Peoples R China

来源：

INFORMATION SCIENCES | 2023年 / 630卷

关键词：

Interval-valued data; Hierarchical priority structure; Two-dimensional Gaussian membership; function; REGRESSION; SYSTEMS; PREDICTION;

D O I：

10.1016/j.ins.2023.02.026

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a new two-dimensional gaussian hierarchical priority fuzzy system (TGHPFS) is proposed to handle interval-valued data. TGHPFS first performs hierarchical clustering of the average value of interval-valued data in each dimension to generate two-dimensional gaussian membership functions of two-level rules. The two levels of rules are associated by calculating the activation strength of the second-level rules to the first-level rules and setting the connection threshold. The regularized least squares method is used to optimize the consequents of the second-level rules. The two-dimensional gaussian membership function designed in this paper is used to model the antecedents of interval-valued data, solving the correlation problem between the left and right values of interval-valued data. The effectiveness of TGHPFS is validated using real-world datasets, and the proposed method is compared with other latest methods to show the superiority of TGHPFS.

引用

页码：23 / 39

页数：17

共 41 条

[1] A Specificity-Based Approach to Semantic Interpretation and Hierarchical Complexity Reduction in Fuzzy Models [J].

Adel-Rastkhiz, Ehsan ;

Akbarzadeh-T, Mohammad-R. .

IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2021, 29 (09) :2661-2674

[2]

Alcalá-Fdez J, 2011, J MULT-VALUED LOG S, V17, P255

[3] A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data [J].

Antonio Sanz, Jose ;

Bernardo, Dario ;

Herrera, Francisco ;

Bustince, Humberto ;

Hagras, Hani .

IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2015, 23 (04) :973-990

[4]

Billard L, 2000, ST CLASS DAT ANAL, P369

[5]

Billard L., 2002, Classification, clustering, and data analysis: recent advances and applications, P281, DOI 10.1007/978-3-642-56181-8_31

[6] Interval-valued fuzzy regression: Philosophical and methodological issues [J].

Boukezzoula, Reda ;

Coquin, Didier .

APPLIED SOFT COMPUTING, 2021, 103

[7] Recent trends in intelligent data analysis [J].

Corchado, Emilio ;

Wozniak, Michal ;

Abraham, Ajith ;

de Carvalho, Andre C. P. L. F. ;

Snasel, Vaclav .

NEUROCOMPUTING, 2014, 126 :1-2

[8] A clusterwise nonlinear regression algorithm for interval-valued data [J].

de Carvalho, Francisco de A. T. ;

Lima Neto, Eufrasio de A. ;

da Silva, Kassio C. F. .

INFORMATION SCIENCES, 2021, 555 :357-385

[9] Lasso-constrained regression analysis for interval-valued data [J].

Giordani, Paolo .

ADVANCES IN DATA ANALYSIS AND CLASSIFICATION, 2015, 9 (01) :5-19

[10]

Kabir C. Wagner, 2021, 2021 IEEE INT C FUZZ, P1

← 1 2 3 4 5 →