摘要
针对传统葡萄酒品质分类中低品质类葡萄酒样本识别率低的问题,提出一种基于集成支持向量机的葡萄酒品质分类优化算法.首先,通过"一对多"支持向量机实现多分类;其次,把支持向量机作为基分类器,反复训练支持向量机分类样本,通过AdaBoost得到多个支持向量机基分类器组合的强分类器,运用AdaBoost算法动态调整样本权值,适当提高低品质类样本权重,使低品质类中错判的样本代价增大,从而改进不平衡样本分类性能;最后,以Wine Quality数据集为研究对象,建立以多分类器优化集成为核心的葡萄酒品质分类模型.仿真结果表明,与传统的SVM算法相比,所提方法显著提高了低品质类葡萄酒分类精度.
Focused on the issue that traditional classification algorithms for wine quality clas-sification have a low recognition rate to low-quality wines, an optimization algorithm based on ensemble Support Vector Machine （SVM） was proposed. Firstly, muti-class was accom-plished by 1-against-the rest SVM ; Secondly, SVM was repeatedly trained as weaker classifier and a strong classifier was gotten by grouping a number of base classifiers based on SVM. The sample weight were dynamically adjusted by using AdaBoost algorithm, the sample weight of low quality were appropriately increased, and then the cost of misjudge samples was also increased for improving classification performance of unbalanced datasets ; Finally, the wine quality datasets of UCI database was taken as research object, the classification model of wines quality was established that using muti-classifiers optimal integration as the core. The simulation results show that compared with the standard SVM algorithm, classifi-cation accuracy of low quality wine was significantly improved based on AdaBoost-SVM.
作者
杨云
卢美静
穆天红
YANG Yun LU Meijing MU Tian-hong(College of Electrical and Information Engineering, Shaanxi University of Science Technology, Xi’an 710021,China Qinghai Agriculture and Animal Husbandry Market Information Center, Xining 810008, China)
出处
《陕西科技大学学报（自然科学版）》
2017年第1期178-182,187,共6页
Journal of Shaanxi University of Science & Technology
基金
陕西省科技厅社会发展科技攻关计划项目(2015SF277
2016SF-444)
陕西省科技厅科学技术研究发展计划项目(2014K15-03V06)
西安市科技计划项目(NC1403(2)
NC1319(1))
关键词
分类
支持向量机
集成学习
葡萄酒品质
不平衡数据
classification
support vector machine
ensemble learning
wine quality
unbal-anced data
