A neighborhood rough set model for attribute reduction without distance metric

Xingxin Chen; Shuyin Xia; Feng Hu

doi:10.1117/12.2624145

19 December 2021 A neighborhood rough set model for attribute reduction without distance metric

Xingxin Chen, Shuyin Xia, Feng Hu

Proceedings Volume 12128, Second International Conference on Industrial IoT, Big Data, and Supply Chain; 121280P (2021) https://doi.org/10.1117/12.2624145
Event: 2nd International Conference on Industrial IoT, Big Data, and Supply Chain, 2021, Macao, China

Abstract

Neighborhood rough set (NRS) is an important extension of rough set theory, which can process continuous data directly without any prior knowledge. As far as we know, all present neighborhood rough set models are defined on distance metric (usually Euclidean distance), which makes neighborhood rough set model invalid in high-dimensional space due to "Curse of Dimensionality". Even in low-dimensional space, the performance of this model will be degraded due to the neglect of attribute weight by distance metric. This paper proposes a novel neighborhood rough set model based on space partition, and designs an attribute reduction algorithm based on this model. Experimental results on UCI benchmark datasets show that our algorithm performs better than the state-of-the-art NRS algorithms.

Citation Download Citation

Xingxin Chen, Shuyin Xia, and Feng Hu "A neighborhood rough set model for attribute reduction without distance metric", Proc. SPIE 12128, Second International Conference on Industrial IoT, Big Data, and Supply Chain, 121280P (19 December 2021); https://doi.org/10.1117/12.2624145

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