Many practical scenarios have demanded that we should classify unlabeled data more accurately based on both physical features (e.g., color, distance, or similarity) and implicit style features of data. As most extant classification algorithms classify unlabeled data based only on their physical features, they become weak in achieving expected classification results for many scenarios. To work around this drawback in this paper, a novel classification method (FuCM) from the perspective of fuzzy social network based on both physical and implicit style features of data is proposed. Based on the proposed fuzzy social network and its dynamics about fuzzy influences of nodes, FuCM comprises two stages. In its training stage, after the fuzzy social network has been built, it learns the topological structure, reflecting physical features and implicit style features of data by carrying out fuzzy influence dynamics in the built network. In its prediction stage, both physical and implicit style features of data are effectively integrated to yield the double structure efficiency characterized by fuzzy influences of nodes. FuCM classifies unlabeled data according to the strongest connection measure based on the proposed double structure efficiency. FuCM does not assume that both data distribution and the classification by physical features or by both physical and implicit style features of data must be known in advance. Thus, it is a novel unified classification framework in this sense. In contrast to all the nine comparative methods, FuCM experimentally demonstrates its comparable classification performance on most synthetic, UCI and KEEL datasets, which can be well classified based only on physical features of data. Furthermore, it displays distinctive superiority on five case studies where satisfactory classification certainly depends on both physical and implicit style features.

中文翻译：

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一种基于数据物理和隐式风格特征的模糊社交网络分类新方法

许多实际场景要求我们根据数据的物理特征（例如，颜色、距离或相似性）和隐式风格特征对未标记的数据进行更准确的分类。由于现有的大多数分类算法仅根据物理特征对未标记的数据进行分类，因此在许多场景下无法达到预期的分类结果。为了解决本文中的这个缺点，从基于数据的物理和隐式风格特征的模糊社交网络的角度提出了一种新的分类方法（FuCM）。基于所提出的模糊社交网络及其关于节点模糊影响的动态，FuCM 包括两个阶段。在训练阶段，模糊社交网络建立后，学习拓扑结构，通过在构建的网络中进行模糊影响动态来反映数据的物理特征和隐式风格特征。在其预测阶段，有效整合数据的物理和隐式风格特征，产生以节点模糊影响为特征的双重结构效率。FuCM 根据提出的双结构效率根据最强连接度量对未标记数据进行分类。FuCM 并不假设必须事先知道数据的分布和按物理特征或按物理和隐式风格特征进行的数据分类。因此，从这个意义上说，它是一个新颖的统一分类框架。与所有九种比较方法相比，FuCM 在大多数合成、UCI 和 KEEL 数据集上通过实验证明了其可比的分类性能，仅根据数据的物理特征就可以很好地分类。此外，它在五个案例研究中显示出独特的优势，其中令人满意的分类肯定取决于物理和隐式风格特征。