Feature selection in unsupervised context: Clustering based approach

被引：0

作者：

Klepaczko, A ^{[1
]}

Materka, A ^{[1
]}

机构：

[1] Tech Univ Lodz, PL-90924 Lodz, Poland

来源：

Computer Recognition Systems, Proceedings | 2005年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we present a novel feature selection method that is applicable in unsupervised learning tasks. The method is based on clustering quality measures, which reflect different aspects of clustering performance. Sequential Floating Forward Search algorithm is employed to search through the original feature space for the best possible subset. Main stress has been put on the objectivism of the new technique, so that it could be applied in various classification tasks. Results of experiments with texture images are presented in order to confirm effectiveness of the method.

引用

页码：219 / 226

页数：8

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