A Multi-Granular Relative Density Model for Class Noise Detection
Xiao Liang, Shuyin Xia, Qun Liu, Yunsheng Liu, Baiyun Chen, Guoyin Wang · 2018
Class noise can cause overfitting of learning methods and deteriorate their generalizability. Although relative density can detect class noise effectively, it has a time complexity of O(N2) and a low efficiency. To address this problem, by introducing the granular computing into the relative density model, this paper proposes a Multi-Granularity Relative Density (MGRD) model for class noise detection. In an experiment, we tested the effect of parameters on its performance under classifiers. The experimental results on benchmark data sets demonstrated that it had a higher efficiency than conventional methods. In addition, it exhibited better generalizability in comparison with the conventional methods because of the good robustness of granular computing on many cases.