计算机与数字工程2024，Vol.52Issue(2)：559-566,8.DOI:10.3969/j.issn.1672-9722.2024.02.047
基于Fisher鉴别分析和集成学习的烟叶分级方法
Tobacco-leaf Ranking Method Based on Fisher Discriminant Analysis and Ensemble Learning
顾晓东 1李和平 2刘金云 2陈龙 2王艺斌 3魏新亮 2符再德 4邓斌 2周文辉 2黄振军 2袁志明2
作者信息
- 1. 南京理工大学计算机科学与工程学院 南京 210094
- 2. 湖南中烟工业有限责任公司 长沙 430100
- 3. 南京焦耳科技有限责任公司 南京 210032
- 4. 湘西鹤盛原烟发展有限责任公司 吉首 416000
- 折叠
摘要
Abstract
Intelligent tobacco-leaf ranking is crucial for the quality of cigarette production.To further improve the accuracy of tobacco-leaf ranking,a method of jointly utilizing three leaf views of front,back and perspective is proposed.For each of the three views,texture,color and shape features of tobacco leaves are extracted as the input of the model,and then the feature dimensionali-ty is reduced by linear discriminant analysis.SVM is used as the base classifier,and multiple SVMs are integrated using bagging method to form a ensemble learning based tobacco ranking model.To integrate the features from the three views,the ensemble mod-el is further improved in ranking accuracy.The experimental results show that for only the front view the average accuracy of the pro-posed method achieves 71.39%on the five real tobacco-leaf datasets,which outperforms several existing methods.After fusing joint features of the three views,the accuracy is up amount to 74.8%.关键词
烟叶分级/SVM/线性判别分析/集成学习/袋装法Key words
tobacco grading/SVM/linear discriminant analysis/ensemble learning/bagging method分类
资源环境引用本文复制引用
顾晓东,李和平,刘金云,陈龙,王艺斌,魏新亮,符再德,邓斌,周文辉,黄振军,袁志明..基于Fisher鉴别分析和集成学习的烟叶分级方法[J].计算机与数字工程,2024,52(2):559-566,8.
