摘要
结合粗糙集的属性约简和支持向量机的分类机理,提出了一种混合算法。应用粗糙集理论的属性约简过程作为预处理器,可以把冗余的属性和冲突的对象从决策表中删去,但不损失任何有效信息;然后基于支持向量机进行分类建模和预测。这样可以大大降低数据维数,降低支持向量机分类过程中的复杂度,减少占用的存储空间,并在不同程度上避免了训练模型的过拟合现象,但分类性能并不会降低。最后的仿真实例说明了所提方法的有效性。
In this paper we present a novel hybrid algorithm based on attribute reduction of RS and classification principles of SVM. Firstly, the attribute reduction of RS has been applied as preprocessor so that we can delete redundant attributes and conflicting objects from decision making table but remain efficient information lossless. Then, we realize classification modeling and forecasting test based on SVM. By this method, we can greatly reduce the dimension of data, highly decrease the complexity in the process of SVM classification cut down the occupied memory, and prevent the over-fit of training model at a certain extent, but obtain the good classification performance. Finally, the simulation experiments show the effectiveness of the suggested hybrid method.
出处
《计算机应用》
CSCD
北大核心
2004年第3期65-67,70,共4页
journal of Computer Applications
关键词
粗糙集
支持向量机
核函数
属性约简
Support Vector Machine(SVM)
kernel function
Rough Set(RS)
attribute reduction
