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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 language | English |
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Pages (from-to) | 69-91 |
Number of pages | 23 |
Journal | Information Sciences |
Volume | 580 |
Early online date | 30 Aug 2021 |
DOIs | |
Publication status | Published - 01 Nov 2021 |
Keywords
- Attribute reduction
- Classification
- Earth Mover's Distance
- Inconsistency
- Robustness
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Dive into the research topics of 'Inconsistency guided robust attribute reduction'. Together they form a unique fingerprint.Projects
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Ser Cymru: Reconstruction of Missing Information in Optical Remote Sensing Images Based on Deep Learning and Knowledge Interpolation
Shen, Q. (PI)
01 Oct 2020 → 28 Feb 2023
Project: Externally funded research