access icon free Feature selection based on geometric distance for high-dimensional data

A novel feature selection method based on geometric distance is proposed. It utilises both the average distance between classes along with the evenness of these distances to evaluate feature subsets. The feature evaluation and selection process used therein is very easy to understand, because it lends itself to a simple geometrical analysis. Moreover, because the method does not calculate the relevance or redundancy between features, it is faster than other filter methods that use information or statistical dependency concepts. The experiments demonstrate its markedly better classification performance as well as fast computation compared with existing methods.

Inspec keywords: feature selection; statistical analysis; set theory

Other keywords: feature subset evaluation; feature evaluation process; high-dimensional data; feature selection method; statistical dependency concepts; information dependency concepts; feature selection process; geometric distance; geometrical analysis

Subjects: Pattern recognition; Other topics in statistics; Combinatorial mathematics

References

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http://iet.metastore.ingenta.com/content/journals/10.1049/el.2015.4172
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