期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 28, 期 2, 页码 361-375出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2019.2906855
关键词
Social networking (online); Testing; Task analysis; Correlation; Support vector machines; Fuzzy sets; Media; Data classification; fuzzy influences; fuzzy social networks; implicit style features; physical features
资金
- National Natural Science Foundation of China [61572236, 61300151]
- NSFC-JSPS Grant [6161101250]
- Natural Science Foundation of Jiangsu Province [BK20161268]
- Fundamental Research Funds for the Central Universities [JUDCF13030]
- National First-Class Discipline Program of Light Industry and Engineering (LITE2018)
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.
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