Abstract: The combination operators of multiple classifiers system are fixed combination operator with relatively poor serviceability.  The idea of flexibility of universal logic theory is introduced in multiple classifiers system, and a universal combination rule based on universal combination operation model is proposed. Universal combination rule is suitable to multiple classifiers system with parallel structure. Then genetic algorithm is used to estimate parameters of universal combination rule. The experimental results on time series datasets show that the classification performance of universal combination rule is better than that of fixed combination rules, which are product rule, mean rule, median rule, max rule, min rule and majority vote rule.

Key words: universal combination rule, multiple classifiers system, universal combination operation model, genetic algorithm

摘要： 现有的多分类器系统采用固定的组合算子，适用性较差。将泛逻辑的柔性化思想引入多分类器系统中，应用泛组合运算模型建立了泛组合规则。泛组合规则采用遗传算法进行参数估计，对并行结构的多分类器系统具有良好的适用性。在时间序列数据集上的分类实验结果表明，泛组合规则的分类性能优于乘积规则、均值规则、中值规则、最大规则、最小规则、投票规则等固定组合规则。

关键词: 泛组合规则, 多分类器系统, 泛组合运算模型, 遗传算法