Rough Neighborhood Covering Reduction for robust classification
Wei Feng Huang, Xiaodong Yue, Caiming Zhong, Nan Zhang · 2016
Neighborhood Covering Reduction (NCR) is an effective tool to learn rules from structural data for classification. However, the existing neighborhood covering model is not robust enough. A neighborhood is constructed according to the nearest heterogeneous samples. This strategy over focuses on the boundary samples and makes the model sensitive to noise. To tackle this problem, we proposed a Rough Neighborhood Covering Reduction method (RCR) for robust classification. In RCR method, we construct an approximation of neighborhood based on rough sets and further design a reduction algorithm to filter out neighborhoods to form flexible covering of data space for classification. Abundant experiments verify the robustness of the proposed method, which achieves precise and stable classification results on noisy data.