中国农业科学院农业信息研究所 中国农学会
Agricultural Information Institute of CAAS China Association of Agricultural Science Societies
En
Ch
中国农业科学院农业信息研究所 中国农学会 主办

AI检索

订阅

返回顶部
当前位置: 首页 > 文章 > 基于集成学习的农业生产技术效率评价方法 农业机械学报 2021 (S1) 148-155
Position: Home > Articles > Evaluation Method of Agricultural Production Technical Efficiency Based on Ensemble Learning Transactions of the Chinese Society for Agricultural Machinery 2021 (S1) 148-155

基于集成学习的农业生产技术效率评价方法

作  者：
冯建英;苏允汇;龚劭齐;王智;穆维松
单  位：
中国农业大学信息与电气工程学院
关键词：
农业生产;技术效率;效率层级分析;数据包络分析;多模型融合;Stacking集成学习
摘  要：
提高农业生产技术效率是推动农业高质量发展的重要内容,但传统的基于前沿面的技术效率评价模型在实际应用中存在模型运算速度慢和灵活性低等问题,难以对大量新增样本的效率进行快速评价。基于此,本研究将基于前沿面的DEA技术效率测算模型与集成学习模型相结合,提出一种农业生产技术效率的评估预测方法,并利用葡萄生产技术效率数据集验证了模型的效果。研究结果显示,Stacking融合模型的准确率和AUC分别达到了94.8%和0.984,均优于其他对比模型,表明基于Stacking集成学习模型具有较高的预测准确性,能够实现更加高效、快速的技术效率评价。
译  名：
Evaluation Method of Agricultural Production Technical Efficiency Based on Ensemble Learning
作  者：
FENG Jianying;SU Yunhui;GONG Shaoqi;WANG Zhi;MU Weisong;College of Information and Electrical Engineering, China Agricultural University;
关键词：
agricultural production;;technical efficiency;;efficiency tier analysis;;DEA;;multi-model fusion;;Stacking ensemble learning
摘  要：
Improving the technical efficiency of agricultural production is an important part to promote the high-quality development of agriculture. However, in practical application, there exist some flaws in the traditional technical efficiency evaluation model based on the frontier, such as slow computing speed and low flexibility, which make it difficult to evaluate the efficiency of a large number of new samples. For the above reasons, a method for evaluating and predicting the technical efficiency of agricultural production was proposed, which combined the DEA technical efficiency measurement model based on the frontier with the ensemble learning model, and the grape production technical efficiency dataset was used to verify the effect of the model. Experiments showed that the Stacking fusion model reached the accuracy and AUC of 94.8% and 0.984 respectively, with promising result that surpassed the other comparison models, indicating that the Stacking ensemble learning model had high accuracy, robustness and generalization ability, and can achieve more efficient, fast and stable technical efficiency evaluation.

相似文章

导出引用
计量
文章访问数: 20
HTML全文浏览量: 0
PDF下载量: 0

所属期刊

推荐期刊

友情链接Friendly link
农业信息研究所Agricultural information institute 农业智能知识服务平台Agricultural intelligent knowledge service platform 农业学术服务平台Agricultural Knowledge Service System 中国知网CNKI 维普网VIP 万方WANFANG DATA
联系我们Contact us

电话Tell：010-82109652-808

邮箱E-mail：agrijournal@caas.cn

服务Service
关于我们About us 意见建议Opinions and suggest
版权说明Copyright
单位：中国农业科学院农业信息研究所 中国农学会 © 2017中国农业科学院农业信息研究所 ICP备案号：京ICP备09089781号-46