Fuzzy rule-based classification has been studied by a number of classification architectures. In this study, hypersphere information granules are used to form initial fuzzy classification model in an intuitive and interpretative way. The principle of justifiable granularity offers a certain way to optimizing information granules while facing the coverage and specificity criteria. By engaging a synergy of the principle of justifiable granularity and migrating prototypes, the refined classification model is constructed for better classification performance. A series of experiments concerning synthetic datasets and comparative studies are also implemented to exhibit the feasibility and effectiveness of the proposed classification method.
CITATION STYLE
Fu, C., & Lu, W. (2019). Fuzzy Rule-Based Classification with Hypersphere Information Granules. In Advances in Intelligent Systems and Computing (Vol. 1000, pp. 258–269). Springer Verlag. https://doi.org/10.1007/978-3-030-21920-8_24
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