Inconsistency guided robust attribute reduction

Yanpeng Qu*, Zheng Xu, Changjing Shang, Xiaolong Ge, Ansheng Deng, Qiang Shen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (SciVal)
46 Downloads (Pure)

Abstract

Attribute reduction (AR) plays an important role in reducing irrelevant and redundant domain attributes, while maintaining the underlying semantics of retained ones. Based on Earth Mover's Distance (EMD), this paper presents a robust AR algorithm from the perspective of minimising the inconsistency between the discernibility of the reduct and the entire original attribute set. Due to the susceptibility of the inconsistency gauger to noisy information, a strategy for instance denoising is also proposed by detecting abnormal local class distributions with regard to the global class distribution. With such a pretreatment process for AR, the robustness of the reduct found is significantly improved, as testified by systematic experimental investigations. The experimental results demonstrate that the reduct gained by the proposed approach generally outperforms those attained by the application of popular, state-of-the-art AR techniques, in terms of both the size of attribute reduction and the classification results using the reduced attributes.

Original languageEnglish
Pages (from-to)69-91
Number of pages23
JournalInformation Sciences
Volume580
Early online date30 Aug 2021
DOIs
Publication statusPublished - 01 Nov 2021

Keywords

  • Attribute reduction
  • Classification
  • Earth Mover's Distance
  • Inconsistency
  • Robustness

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