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Received:March 20, 2017 Revised:May 09, 2017
Received:March 20, 2017 Revised:May 09, 2017
中文摘要: 为了提高育种领域选种的准确率同时缩短品种培育年限，利用改进的随机森林算法根据小麦育种历史数据构建评价模型. 在训练分类器之前，利用改进的SMOTE算法来改善训练样本集中的非平衡现象；在基分类器训练完成后，测试单个分类器的性能并剔除性能较差的基分类器，实现随机森林中基分类器的筛选. 实验结果表明，文中提出的算法在小麦种质评价方面取得了不错的效果，可以辅助育种工作者进行品种选育.
中文关键词: 小麦育种评价 非平衡数据集 随机森林 改进的SMOTE方法
Abstract:In order to improve the accuracy of seed selection and shorten the cultivation period of cultivars, the improved random forest algorithm is used to construct the evaluation model of the history data of wheat breeding. Before training the classifiers, the improved SMOTE algorithm is used to improve the non-balance of the training samples. After the training of the base classifiers, we test every classifier's performance and delete bad classifiers to realize the screening of the base classifier in random forest. The experimental results show that the proposed algorithm has achieved good results in wheat germplasm evaluation, which can help to breed varieties.
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基金项目:中国科学院战略性先导科技专项（XDA08040110）
引用文本：
邹永潘,王儒敬,李伟.随机森林算法在小麦育种辅助评价中的应用.计算机系统应用,2017,26(12):181-185
ZOU Yong-Pan,WANG Ru-Jing,LI Wei.Application of the Random Forest Algorithm in Wheat Breeding Evaluation.COMPUTER SYSTEMS APPLICATIONS,2017,26(12):181-185
邹永潘,王儒敬,李伟.随机森林算法在小麦育种辅助评价中的应用.计算机系统应用,2017,26(12):181-185
ZOU Yong-Pan,WANG Ru-Jing,LI Wei.Application of the Random Forest Algorithm in Wheat Breeding Evaluation.COMPUTER SYSTEMS APPLICATIONS,2017,26(12):181-185
