Comparison of two Feature Selection Algorithms Oriented to Raw Cotton Ripeness Discrimination

doi:10.3788/OPE.20132108.2121

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Comparison of two Feature Selection Algorithms Oriented to Raw Cotton Ripeness Discrimination

更新时间：2020-08-12

- Comparison of two Feature Selection Algorithms Oriented to Raw Cotton Ripeness Discrimination
- Optics and Precision Engineering Vol. 21, Issue 8, Pages: 2121-2128(2013)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20132108.2121
  CLC： TP391.4
- Received：06 February 2013，
  
  Revised：06 April 2013，
  
  Published Online：20 August 2013，
  
  Published：15 August 2013
- 稿件说明：
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王玲刘德营姬长英. 面向田间籽棉成熟度判别的二种特征选择算法比较[J]. 光学精密工程, 2013,21(8): 2121-2128

WANG Ling LIU De-ying JI Chang-ying. Comparison of two Feature Selection Algorithms Oriented to Raw Cotton Ripeness Discrimination[J]. Editorial Office of Optics and Precision Engineering, 2013,21(8): 2121-2128
王玲刘德营姬长英. 面向田间籽棉成熟度判别的二种特征选择算法比较[J]. 光学精密工程, 2013,21(8): 2121-2128 DOI： 10.3788/OPE.20132108.2121.

WANG Ling LIU De-ying JI Chang-ying. Comparison of two Feature Selection Algorithms Oriented to Raw Cotton Ripeness Discrimination[J]. Editorial Office of Optics and Precision Engineering, 2013,21(8): 2121-2128 DOI： 10.3788/OPE.20132108.2121.

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

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。

Abstract

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 feature subset selection algorithms such as Wrapper-based Exhaustive searching and Wrapper-based stopping(WE-W) and Filter-based Heuristic searching and Wrapper-based stopping(FH-W) were compared 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 (l=1

15) feature subset was searched by using exhaustive (WE-W) and heuristic (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 its higher execute efficiency and good classification accuracy.

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references

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