CAJ | 학술논문

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。
재공업생산분류과정중，존재수거량과대，용여특정과다적문제，본문결합모호조조집화지지향량궤연구료일충분류산법。수선채용모호조조집방법대조건속성진행속성약간，조출대분류결책구유주요영향적특정。이약간결과작위분류모형적수입변량，연후이용지지향량궤대양본진행훈련，건립분류모형，최후장본문적방법용우지판정반면분류화분석양화려정충분해과정，병측시모형적분류효과。MATLAB방진실험적결과표명본문적방법시유효적，구유분류정학솔고，결구간단，범화능력호적우점。
A classification algorithm is studied based on the fuzzy rough set and support vector machine about industrial processes which has too much redundant features and large amount of data in this paper. Condition features are reduced by fuzzy rough set, and find the characters of has a major effect on classification of the floor. With the reduction result as input variables, SVM is used to train samples, establish classification model, then this method is applied to classification of the floor and alumina seed decomposition process, also test classification results. MATLAB Simulation experiment results show that the method is effective, high classification accuracy and has the advantages of simple structure, good generalization ability.