Feature selection with data field

Hanning Yuan, Shuliang Wang*, Ying Li, Jinghua Fan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

A new feature selection method is proposed for high-dimensional data clustering on the basis of data field. With the potential entropy to evaluate the importance of feature subsets, features are filtered by removing unimportant features or noises from the original datasets. Experiments show that the proposed method can sharply reduce the number of dimensions and effectively improve the clustering performance on WDBC dataset.

Original languageEnglish
Pages (from-to)661-665
Number of pages5
JournalChinese Journal of Electronics
Volume23
Issue number4
Publication statusPublished - 1 Oct 2014

Keywords

  • Data field
  • Dimension reduction
  • Feature selection
  • High-dimensional objects
  • Potential entropy

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