En hälsomanual till Gud : - En beskrivande idéanalys av sjundedagsadventismens syn på hälsa
提出一种基于模糊多邻域粗糙集模型的特征选择方法，同时考虑特征间的相关性、冗余性、交互性和互补性，在多个数据集上有效提升分类性能并降低特征维度。
Feature selection has been studied by many researchers using information theory to select the most informative features. Up to now, however, little attention has been paid to the interactivity and complementarity between features and their relationships. In addition, most of the approaches do not cope well with fuzzy and uncertain data and are not adaptable to the distribution characteristics of data. Therefore, to make up for these two deficiencies, a novel interactive and complementary feature selection approach based on fuzzy multineighborhood rough set model (ICFS_FmNRS) is proposed. First, fuzzy multineighborhood granules are constructed to better adapt to the data distribution. Second, feature multicorrelations (i.e., relevancy, redundancy, interactivity, and complementarity) are considered and defined comprehensively using fuzzy multigranularity uncertainty measures. Next, the features with interactivity and complementarity are mined by the forward iterative selection strategy. Finally, compared with the benchmark approaches on several datasets, the experimental results show that ICFS_FmNRS effectively improves the classification performance of feature subsets while reducing the dimension of feature space.