CLASSIFICATION BY FUZZY INTEGRAL - PERFORMANCE AND TESTS

被引：179

作者：

GRABISCH, M

NICOLAS, JM

机构：

[1] Thomson-CSF, Central Research Laboratory, Domaine de Corbeville

来源：

FUZZY SETS AND SYSTEMS | 1994年 / 65卷 / 2-3期

关键词：

PATTERN RECOGNITION; MULTICRITERIA DECISION; FUZZY PATTERN MATCHING; FUZZY MEASURE; FUZZY INTEGRAL;

D O I：

10.1016/0165-0114(94)90023-X

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper presents an attempt to characterize the performance of methods of classification based on fuzzy integral. After an introductory explanation about the approach, a lower bound of the minimal number of training samples is found, and it is shown that a minimum squared error criterion leads to the best approximate for the optimal Bayes classifier. Some tests on simulated and real data are provided.

引用

页码：255 / 271

页数：17

共 22 条

[11] A FUZZY K-NEAREST NEIGHBOR ALGORITHM [J].