Non-unique decision differential entropy-based feature selection

Yanpeng Qu, Rong Li, Ansheng Deng, Changjing Shang, Qiang Shen

Research output: Contribution to journalArticlepeer-review

10 Citations (SciVal)
176 Downloads (Pure)

Abstract

Feature selection plays an important role in reducing irrelevant and redundant features, while retaining the underlying semantics of selected ones. An effective feature selection method is expected to result in a significantly reduced subset of the original features without sacrificing the quality of problem-solving (e.g., classification). In this paper, a non-unique decision measure is proposed that captures the degree of a given feature subset being relevant to different categories. This helps to represent the uncertainty information in the boundary region of a granular model, such as rough sets or fuzzy-rough sets in an efficient manner. Based on this measure, the paper further introduce a differentiation entropy as an evaluator of feature subsets to implement a novel feature selection algorithm. The resulting feature selection method is capable of dealing with either nominal or real-valued data. Experimental results on both benchmark data sets and a real application problem demonstrate that the features selected by the proposed approach outperform those attained by state-of-the-art feature selection techniques, in terms of both the size of feature reduction and the classification accuracy.
Original languageEnglish
Pages (from-to)187-193
Number of pages7
JournalNeurocomputing
Volume393
Early online date04 May 2020
DOIs
Publication statusPublished - 14 Jun 2020

Keywords

  • Differentiation entropy
  • Feature selection
  • Non-unique decision

Fingerprint

Dive into the research topics of 'Non-unique decision differential entropy-based feature selection'. Together they form a unique fingerprint.

Cite this