面向混合数据的多伴随三支决策

针对混合数据的知识表示和分类的问题，在思考混合数据的有效表示时，提出代价敏感多伴随模糊粗糙集模型，在解决混合数据的分类问题上，引入三支决策思想，同时在多伴随模型基础上做了两点改进:1）提出贴近代价敏感多伴随模糊粗糙集模型特点的概率定义；2）借助双量化延迟代价目标函数的思想，构造面向混合数据的新型三支决策模型。该模型具有如下特点:1）引入多个伴随对，模拟了数值型属性和符号型属性之间异构互补的关系；2）定义多伴随算子，充分表达了不同类型属性之间的偏好；3）结合模糊粗糙集，克服了分类问题的不确定性；4）考虑获取不同类型属性的代价，提高了应用到实际生活的可能性。最后用实例验证了此模型的有效性。

Multi-adjoint three-way decisions on heterogeneous data

Considering the problem of knowledge representation and classification relating to heterogeneous data, a cost-sensitive multi-adjoint fuzzy rough set model is proposed for the effective representation of heterogeneous data and in order to solve the classification problem of heterogeneous data, the idea of three-way decisions is introduced. Moreover, two improvements are made on the basis of the multi-adjoint model: 1) A revised probability definition is presented to approximately characterize the cost-sensitive fuzzy rough set model. 2) Based on the idea of the dual quantization delay cost objective function, a novel three-way decisions model is constructed for heterogeneous data. This model has the following characteristics: 1) Multiple adjoint pairs are introduced to simulate the relationship of heterogeneous complementarity between numerical attribute and categorical attribute. 2) The multi-adjoint operator is defined to fully express the preference among different attributes. 3) A fuzzy rough set is combined to overcome the uncertainty of the classification problem. 4) The cost of acquiring both numerical and categorical attributes is considered to improve the possibility of application to real life. The effectiveness of the model is verified in the heterogeneous dataset.