Symbolic classification, clustering and fuzzy radial basis function network

被引：28
|
作者：
Mali, K
Mitra, S
机构：
[1] Indian Stat Inst, Machine Intelligence Unit, Kolkata 700108, W Bengal, India
[2] Kalyani Univ, Dept Comp Sci, Kalyani 741235, W Bengal, India
来源：
FUZZY SETS AND SYSTEMS | 2005年 / 152卷 / 03期
关键词：
radial basis function network; fuzzy clustering; symbolic object; symbolic classification; fuzzy classification; validity index;
D O I：
10.1016/j.fss.2004.10.001
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Symbolic fuzzy classification is proposed using fuzzy radial basis function network, with fuzzy c-medoids clustering at the hidden layer. Symbolic objects include linguistic, nominal, boolean and interval-type of features, along with quantitative attributes. Classification and clustering in this domain involve the use of symbolic dissimilarity between the objects. Fuzzy memberships are used for appropriately handling uncertainty inherent in real-life decisions. The fuzzy radial basis function (FRBF) network here comprises an integration of the principles of radial basis function (RBF) network and fuzzy c-medoids clustering, for handling non-numeric data. The optimal number of hidden nodes is determined by using clustering validity indices, like normalized modified Hubert's statistic and Davies-Bouldin index, in the symbolic framework. The effectiveness of the symbolic fuzzy classification is demonstrated on real-life benchmark data sets. Comparison is provided with the performance of a decision tree. (c) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码：553 / 564
页数：12
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