Sample Set Reduction Method Based on Neighborhood Non-Dominated Crowding-Distance Sorting
Mengmeng Li, Zhigang Shang, Xiaoyang Shen, Caitong Yue, Haofeng Wang, Yonghui Dong, Hong Wan · 2018
To remove the redundant samples and reduce sample set size of big data analysis while ensuring classification accuracy, a sample set reduction method based on Neighborhood Non-dominated Crowding-distance Sorting (NNCS) is proposed. The proposed method is based on the idea of neighborhood, in which the distance between the samples of one class and the other classes is taken as the evaluation criterion. Furthermore, optimization algorithm NSGA-II is adapted to select the key samples by non-dominated crowding-distance sorting and the purpose of reducing the sample set is achieved finally. The experimental results on nine UCI standard data sets show that the method has good generalization performance and reduces the sample set size effectively under the premise of fully guaranteed classification accuracy.