Inconsistency guided robust attribute reduction

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. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a robust attribute reduction algorithm based on Earth Mover's Distance (EMD), which optimizes the reduction results by denoising instances, leading to superior performance in terms of reduction size and classification results compared to other state-of-the-art techniques.