Multiscale Fuzzy Entropy-Based Feature Selection

In practice, it is common that there will be the same decision results under different scale conditions. Therefore, knowledge representation based on a single scale feature framework is far from meeting the needs of practical applications. Based on this, multiscale data have received extensive attention. Feature selection is an important application of fuzzy multigranularity data analysis model. The existing multiscale fuzzy granulation-based feature selection methods remove redundant or irrelevant features by selecting the optimal scale. However, this will lose the information corresponding to the remaining scale fuzzy granules, which will affect the classification results or learning tasks. Inspired by this, multiscale fuzzy entropy is defined to fuse the granule information at different scales, and applied to feature selection. First, the feature with maximum multiscale fuzzy mutual information is first selected. Then, the most significant features are gradually selected by evaluation metric that simultaneously considers the redundancy, relevance, and complementary. A multiscale fuzzy entropy-based feature selection algorithm by means of this evaluation index is further designed. Finally, the proposed method is compared with some state-of-the-art methods. The experimental results show that the proposed algorithm has higher reduction efficiency than the comparison algorithms.