A Construction Method of Fuzzy Classifiers using Confidence-Weighted Learning

被引：1

作者：

Nakashima, Tomoharu ^{[1
]}

Sumitani, Takeshi ^{[1
]}

Bargiela, Andrzej ^{[2
]}

机构：

[1] Osaka Prefecture Univ, Naka Ku, Sakai, Osaka 5998531, Japan

[2] Univ Nottingham, Nottingham NG8 1BB, England

来源：

17TH INTERNATIONAL CONFERENCE IN KNOWLEDGE BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS - KES2013 | 2013年 / 22卷

关键词：

Fuzzy if-then rules; Confidence-weighted; incremental learning; on-line learning;

D O I：

10.1016/j.procs.2013.09.124

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Incremental algorithms for fuzzy classifiers are studied in this paper. It is assumed that not all training patterns are given a priori for training classifiers, but are gradually made available over time. It is also assumed that the previously available training patterns can not be used afterwards. Thus, fuzzy classifiers should be modified by updating already constructed classifiers using the available training patterns. In this paper, a confidence-weighted (CW) learning algorithm is applied to fuzzy classifiers for this task. A series of computational experiments are conducted in order to examine the performance of the proposed method comparing that method with the conventional learning algorithm for fuzzy classifiers. (C) 2013 The Authors. Published by Elsevier B.V.

引用

页码：460 / 466

页数：7

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