面向田间籽棉成熟度判别的二种特征选择算法比较  

Comparison of two feature selection algorithms oriented to raw cotton ripeness discrimination

作　　者：王玲[1] 刘德营[1] 姬长英[1] 

机构地区：[1]南京农业大学工学院/江苏省现代设施农业技术与装备工程实验室,江苏南京210031

出　　处：《光学精密工程》2013年第8期2121-2128,共8页Optics and Precision Engineering

基　　金：国家863高技术研究发展计划资助项目(No;2006AA10Z259);江苏省农机基金资助(No;GXZ10007)

摘　　要：为了快速、准确地判别田间籽棉的成熟度,提取了描述棉瓣形状的15个结构特征,基于10折交叉验证比较了封装器下穷举搜索并基于封装器停止搜索(WE-W)和过滤器下启发式搜索并基于封装器停止搜索(FH-W)这二种特征选择算法的执行效率和分类性能。分别以验证集上Bayes分类器的误分率(WE-W)和训练集上的类可分性测量值(FH-W)为评价函数,在训练集上穷举搜索(WE-W)和启发式搜索(FH-W)最优l维特征子集,l=1,2,…,15,并于Bayes分类器在验证集上的平均误分率极小时停止搜索(WE-W和FH-W)。结果显示,WE-W和FH-W算法在预测集上于l=3处分别获得了85.39%(WE-W)和85.28%(FH-W)的平均识别率,表明FH-W算法执行效率高、分类性能好,对实际应用有参考意义。To discriminate the ripeness of cotton quickly and accurately, 15 shape structure features were extracted from cotton images and the execute efficiencies and classification accuracy of their fea- ture subset selection algorithms such as Wrapper-based Exhaustive searching and Wrapper-based stop- ping（WE-W） and Filter-based Heuristic searching and Wrapper-based stopping（FH-W） were com- pared by using 10-fold cross-validation. By taking the error rate of a Bayes classifier on validation set （WE-W） and the class-separability measuring value on a training set （FH-W）as assessing functions, the optimal l （/---= 1,2,3, ＂-, 15） feature subset was searched by using exhaustive （WE-W） and heu- ristic （FH-W） strategies on the training set, which stops at the minimum error rate of Bayes-classifier on the validation set（WE-W and FH-W）. Experimental results show that the average classification rates of WE-W and FH-W algorithms on the prediction set are 85.39% （WE-W） and 85.28% （FH- W） at l=3, respectively. It concludes that the FH-W algorithm can be a reference in practice for itshigher execute efficiency and good classification accuracy.

关 键 词：籽棉成熟度 封装器 穷举搜索 过滤器 启发式搜索 特征选择 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]