Feature Selection Algorithm Based on K-means Clustering

被引：0

作者：

Tang, Xue ^{[1
]}

Dong, Min ^{[1
]}

Bi, Sheng ^{[1
]}

Pei, Maofeng ^{[1
]}

Cao, Dan ^{[1
]}

Xie, Cheche ^{[1
]}

Chi, Sunhuang ^{[1
]}

机构：

[1] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China

来源：

2017 IEEE 7TH ANNUAL INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (CYBER) | 2017年

关键词：

mechine learning; feature selection; K-means clustering algorithm; REDUNDANCY; RELEVANCE;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In order to improve the performance of the feature selection algorithm, a feature selection algorithm based on K-means clustering is designed. The algorithm makes use of the idea of K-means clustering based on cosine distance to cluster the features, so that the obtained feature subset has strong correlation and no redundancy. The experimental results show that the feature selection algorithm based on K-means clustering has high efficiency for classification tasks and has short running time, so the algorithm has strong practicability for feature selection.

引用

页码：1522 / 1527

页数：6

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