Possibility measure based fuzzy support function machine for set-based fuzzy classifications

被引：7

作者：

Chen, Jiqiang ^{[1
,2
,3
]}

Hu, Qinghua ^{[1
]}

Xue, Xiaoping ^{[2
]}

Ha, Minghu ^{[3
]}

Ma, Litao ^{[3
]}

Zhang, Xuchang ^{[3
]}

Yu, Zhipeng ^{[3
]}

机构：

[1] Tianjin Univ, Sch Comp Sci & Technol, Tianjin 300072, Peoples R China

[2] Harbin Inst Technol, Dept Math, Harbin 150001, Heilongjiang, Peoples R China

[3] Hebei Univ Engn, Sch Math & Phys, Handan 056038, Peoples R China

来源：

INFORMATION SCIENCES | 2019年 / 483卷

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

Support function machine; Possibility measure; Membership degree; Support function; Set-valued data; REPRESENTATION; MANIFOLD;

D O I：

10.1016/j.ins.2019.01.022

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 real-world applications, there are many set-based fuzzy classifications. However, the current researches have some limitations in solving such classifications. Therefore, a method called possibility measure based fuzzy support function machine (PMFSFM) is discussed in this work. Firstly, two notes are provided as improvement of SFM in theoretical and experimental perspective. Secondly, a set-based fuzzy classification in Euclidean space R-d is converted into a function-based task in Banach space C(S) based on support function and membership degree. Thirdly, a fuzzy optimization problem based on possibility measure is derived and some properties are discussed. Subsequently, a PMFSFM for set-based fuzzy classification is constructed, and it can give both the fuzzy class and the membership degree of a given input to the fuzzy class. Experiment results concerning water quality evaluation in fuzzy environment show the effectiveness of PMFSFM. (C) 2019 Published by Elsevier Inc.

引用

页码：192 / 205

页数：14

共 39 条

[1]

[Anonymous], ARXIV180806394

[2]

[Anonymous], 2014, THESIS

[3] Image thresholding based on the EM algorithm and the generalized Gaussian distribution [J].

Bazi, Yakoub ;

Bruzzone, Lorenzo ;

Melgani, Farid .

PATTERN RECOGNITION, 2007, 40 (02) :619-634

[4] A Simple Fuzzy Method to Remove Mixed Gaussian-Impulsive Noise From Color Images [J].

Camarena, Joan-Gerard ;

Gregori, Valentin ;

Morillas, Samuel ;

Sapena, Almanzor .

IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2013, 21 (05) :971-978

[5] Separability of set-valued data sets and existence of support hyperplanes in the support function machine [J].

Chen, Jiqiang ;

Xue, Xiaoping ;

Ma, Litao ;

Ha, Minghu .

INFORMATION SCIENCES, 2018, 430 :432-443

[6] Support function machine for set-based classification with application to water quality evaluation [J].

Chen, Jiqiang ;

Hu, Qinghua ;

Xue, Xiaoping ;

Ha, Minghu ;

Ma, Litao .

INFORMATION SCIENCES, 2017, 388 :48-61

[7] Set-valued samples based support vector regression and its applications [J].

Chen, Jiqiang ;

Pedrycz, Witold ;

Ha, Minghu ;

Ma, Litao .

EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (05) :2502-2509

[8] Fuzzy and possibilistic clustering for fuzzy data [J].

Coppi, Renato ;

D'Urso, Pierpaolo ;

Giordani, Paolo .

COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2012, 56 (04) :915-927

[9]

CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411

[10] Voltage THD Analysis Using Knowledge Discovery in Databases With a Decision Tree Classifier [J].

De Oliveira, Edson Farias ;

De Lima Tostes, Maria Emilia ;

Oliveira De Freitas, Carlos Alberto ;

Leite, Jandecy Cabral .

IEEE ACCESS, 2018, 6 :1177-1188

← 1 2 3 4 →