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在工业生产分类过程中,存在数据量过大,冗余特征过多的问题,本文结合模糊粗糙集和支持向量机研究了一种分类算法。首先采用模糊粗糙集方法对条件属性进行属性约简,找出对分类决策具有主要影响的特征。以约简结果作为分类模型的输入变量,然后利用支持向量机对样本进行训练,建立分类模型,最后将本文的方法用于地板正反面分类和分析氧化铝晶种分解过程,并测试模型的分类效果。MATLAB仿真实验的结果表明本文的方法是有效的,具有分类正确率高,结构简单,泛化能力好的优点。
In the process of industrial production classification, there is a problem of too large amount of data and too many redundant features. A classification algorithm based on fuzzy rough set theory and support vector machine is studied in this paper. Firstly, fuzzy rough set method is used to attribute reduction to the condition attributes to find out the characteristics that have the main influence on the classification decision. Using the result of reduction as the input variable of the classification model, the SVM is used to train the samples to establish the classification model. Finally, the method of this paper is applied to the classification of the front and back of the floor and the analysis of the decomposition process of the alumina seed crystal, effect. The results of MATLAB simulation show that the method in this paper is effective, and has the advantages of high classification accuracy, simple structure and good generalization ability.