文献类型： 外文期刊
作者： Zhao, Xiangyu 1 ; Han, Yanyun 1 ; Liu, Zhongqiang 1 ; Pan, Shouhui 1 ; Wang, Kaiyi 1 ;
作者机构： 1.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
3.Minist Agr, Key Lab Agriinformat, Beijing 100097, Peoples R China
4.Beijing Engn Res Ctr Agr Internet Things, Beijing 100097, Peoples R China
关键词： Breeding evaluation; coupled representation; quantitative phenotypic traits; feature selection; decision support systems
期刊名称：IEEE ACCESS （ 影响因子：3.367； 五年影响因子：3.671 ）
ISSN： 2169-3536
年卷期： 2020 年 8 卷
页码：
收录情况： SCI
摘要： With the rapid development of improved breeding equipment and information technology, computer-aided decision-making in plant breeding evaluation can help solve the problems associated with high-throughput demand and insufficient experience of breeders in modern large-scale field breeding experiments. Many linear models have made great contributions to the development of breeding evaluation although they are based on a wrong assumption of attribute independence. This paper proposes a unified coupled representation that integrates intra-coupled and inter-coupled relationships to capture the interdependence among quantitative traits by addressing coupling context and coupling weights. Moreover, a hybrid scheme of the linear correlation and ordinal relation is introduced to express the coupling relationship with a preset parameter that balances the contributions so as to capture both relative and absolute performance in cultivar selection and breeding evaluation. A framework that includes data preprocessing, coupled data representation, feature selection, prediction model construction, and assisted decision-making is our overall solution for the plant breeding evaluation task. Experiments on real plant breeding data sets demonstrated the effectiveness of coupled representation for elucidating the quantitative phenotypic traits and the advantages of the proposed plant breeding evaluation algorithm compared with benchmark algorithms.
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