摘要
针对当前禽蛋蛋壳无损检测系统存在检测精度不高的问题,提出粗糙集和支持向量机相结合的方法进行分类器的设计。首先,基于粗糙集理论对特征参数集进行属性约简,在约简过程中,利用模糊C均值聚类算法对特征参数进行量化,并基于属性重要性的启发式搜索对条件属性进行约简;然后,在属性约简的基础上完成支持向量机分类器的训练,在训练过程中,通过交叉验证法对分类器模型参数进行了优化。实验结果表明该方法的分类准确率能够达到94.6%,具有良好的工程应用价值。
Aiming at solving the problem of low accuracy existing in the nondestructive testing system for eggshell,a new hybrid scheme of rough sets and support vector machine for classifier designing was proposed.First,redundant characteristic parameters were reduced based on the rough sets theory.During reducing process,the characteristic parameters were quantified by fuzzy C means clustering algorithm,and the condition attributes were reduced via heuristic search according to their own importance.And then,the classifier was trained by support vector machine based on the reduction result.During training process,the classifier model parameters were optimized by cross validation.The experiments show that the accordance rate of the proposed method can reach 94.6%,which has great engineering application perspective.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2009年第3期167-171,共5页
Transactions of the Chinese Society for Agricultural Machinery
基金
湖南工程学院硕博基金资助项目(120641)
关键词
蛋壳
无损检测
支持向量机
粗糙集
Eggshell
Nondestructive testing
Support vector machine
Rough set
