A DYNAMIC FUZZY MEASURE FOR MULTIPLE CLASSIFIER FUSION

被引：1
|
作者：
Li, Xue-Fei [1 ]
Feng, Hui-Min [2 ]
Chen, Jun-Fen [2 ]
Zhang, Ya-Jing [1 ]
机构：
[1] Agr Univ Hebei, Coll Sci, Baoding 071001, Peoples R China
[2] Hebei Univ, Coll Math & Comp Sci, Key Lab Machine Learning & Computat Intelligence, Baoding 071002, Peoples R China
来源：
PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6 | 2009年
关键词：
Multiple classifier; Fusion; Fuzzy measure; Fuzzy integral; Neural network; NEURAL-NETWORKS; COMBINATIONS;
D O I：
10.1109/ICMLC.2009.5212471
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
It has been shown that the fuzzy integral is an effective tool for the fusion of multiple classifiers. Of primary importance in the development of the system is the choice of the measure which embodies the importance of subsets of classifiers. In this paper we propose a method for a dynamic fuzzy measure which will change following the pattern to be classified (data dependent). This method uses the neural network which has good study ability. Our experiment results show that this method make the classification accurate improve.
引用
收藏
页码：504 / +
页数：3
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