最优约简在分类问题中的应用

Application of optimal reduct in classification problem

在机器学习和人工智能中,粗糙集是进行属性维约简的重要理论与方法。但是,对于给定的信息系统,可能存在多个不同的约简,而不同的约简将会导致产生不同的知识。因此,选择最适合的约简成为一个关键的问题。以此为研究目标,通过在信息系统上增加额外的信息-偏序关系,利用此关系指导属性约简的过程,求出该偏序关系下的最优约简,并运用该最优约简对原信息系统进行维约简。通过对相关工作进行比较分析,详细设计并证明了求取该最优约简的算法,并将最优约简运用于分类问题,得到了良好的效果。

In machine learning and artificial intelligence,rough set theory is an important theory for dimension reduction.However,one given information system may have more than one reduct,which will lead to different knowledge.Therefore,it is a critical problem to select one suitable reduct for knowledge discovery.Aiming at this,this paper appends additional information preference relation into information system and uses it to guide the procedure of finding an reduct,which will produce optimal reduct under preference.Also,this paper proves the correctness of the algorithm and uses it to reduce the information system.In subsequent experiment,this paper applies the optimal reduct in classification problem and receives good effect.