农业大数据学报 ›› 2021, Vol. 3 ›› Issue (3): 55-61.doi: 10.19788/j.issn.2096-6369.210306
李怡德1(), 鲁峰1,2(
), 朱勇1, 徐硕1,2, 孙璐1
收稿日期:
2021-05-11
出版日期:
2021-09-26
发布日期:
2021-12-22
通讯作者:
鲁峰
E-mail:liyd@cafs.ac.cn;lufeng@cafs.ac.cn
作者简介:
李怡德,男,助理研究员,硕士,研究方向:渔船管理信息化、数据挖掘等。E-mail:基金资助:
Yide Li1(), Feng Lu1,2(
), Yong Zhu1, Shuo Xu1,2, Lu Sun1
Received:
2021-05-11
Online:
2021-09-26
Published:
2021-12-22
Contact:
Feng Lu
E-mail:liyd@cafs.ac.cn;lufeng@cafs.ac.cn
摘要:
渔船转移是海洋渔船日常管理过程中的一项关键业务,也是所有渔船管理业务中涉及流程最多、数据传递量最大的业务,通过对大量渔船历史转移数据进行处理分析,可挖掘出与渔船转移活动相关的潜在决定性因子,对保障渔民经济利益和制定渔船管理政策等活动具有重要意义。本文基于中国渔政管理指挥系统中的渔船基础数据和渔船转移数据,并以浙江省为典型案例,选取2018年1月至2020年7月共计5641条渔船的历史转移业务数据进行数值化处理。采用梯度提升迭代决策树(GBDT)算法进行分类器逐级迭代,给出了特征分类结果与模型训练集,并最终构建了渔船被交易潜在可能性的单决策树和多决策树模型。通过模型中船龄、船长、船体材质、作业类型等渔船基本参数的权重,分析了渔民购置渔船的倾向性。结果表明:不同类型的渔船,被购置的可能性存在较大的差异,大船长、大吨位、高船龄、拖网及张网作业类型是渔船发生转移的重要决定因子。对比各项特征损失函数计算得到的损失值大小,20年船龄、大中型船长等特征的损失值比其他特征损失值小15%以上,意味着使用所选特征进行计算的分类识别率更高。本研究通过定量化分析渔民购置渔船的倾向性,可在渔船转移过程中最大化保障渔民的经济利益,同时可对渔船管理政策的制定起到辅助决策作用。
中图分类号:
李怡德, 鲁峰, 朱勇, 徐硕, 孙璐. 基于梯度提升迭代决策树模型的渔船转移数据挖掘[J]. 农业大数据学报, 2021, 3(3): 55-61.
Yide Li, Feng Lu, Yong Zhu, Shuo Xu, Lu Sun. Data Mining for Fishing Vessel Purchase Based on Gradient Boosting Decision Tree Algorithm[J]. Journal of Agricultural Big Data, 2021, 3(3): 55-61.
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