Feature Selection using Mutual Information for High-dimensional Data Sets

被引：0

作者：

Nagpal, Arpita ^{[1
]}

Gaur, Deepti ^{[1
]}

Gaur, Seema ^{[2
]}

机构：

[1] ITM Univ, Dept Comp Sci, Gurgaon, India

[2] Banasthali Univ, Banasthali, Rajasthan, India

来源：

SOUVENIR OF THE 2014 IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE (IACC) | 2014年

关键词：

Correlation; feature selection; minimum spanning tree; data set;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

To reduce the dimensionality of dataset, redundant and irrelevant features need to be segregated from multidimensional dataset. To remove these features, one of the feature selection techniques needs to be used. Here, a feature selection technique to remove irrelevant features has been used. Correlation measures based on the concept of mutual information has been adopted to calculate the degree of association between features. In this paper authors are proposing a new algorithm to segregate features from high dimensional data by visualizing relevant features in the form of graph as a dataset.

引用

页码：45 / 49

页数：5