FLSOM with Different Rates for Classification in Imbalanced Datasets

被引：0

作者：

Machon-Gonzalez, Ivan ^{[1
]}

Lopez-Garcia, Hilario ^{[1
]}

机构：

[1] Univ Oviedo, Escuela Politecn Super Ingn, Dept Ingn Elect Elect Computadores & Sistemas, Gijon Xixon 33204, Spain

来源：

ARTIFICIAL NEURAL NETWORKS - ICANN 2008, PT I | 2008年 / 5163卷

关键词：

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

There are several successful approaches dealing with imbalanced datasets. In this paper, the Fuzzy Labeled Self-Organizing Map (FLSOM) is extended to work with that type of data. The proposed approach is based on assigning two different values in the learning rate depending on the data vector membership of the class. The technique is tested with several datasets and compared with other approaches. The results seem to prove that FLSOM with different rates is a suitable tool and allows understanding and visualizing the data such as overlapped clusters.

引用

页码：642 / 651

页数：10

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