关键词: 模糊包含度, 超盒粒, 正评价函数, 模糊格推理, 支持向量机

Abstract: How to divide the training set into the changeable hyperbox granules is one of the key issues in granular computing. Nonlinear positive valuation functions are used to form the fuzzy inclusion measure function between two hyperbox granules. The classifiers including changeable hyperbox granules are formed by the conditional join strategy, which is determined by the threshold of granularity. The experimental results show that the hyperbox granule classifiers improve the test accuracy compared with fuzzy lattice reasoning classifiers and support vector machines, and speed up the training process compared with support vector machines.

Key words: fuzzy inclusion measure, hyperbox granule, positive valuation function, fuzzy lattice reasoning, Support Vector Machines（SVM）