吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (2): 609-614.doi: 10.13229/j.cnki.jdxbgxb201702036

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基于果蝇优化的随机森林预测方法

赵东1, 2, 臧雪柏1, 赵宏伟1   

  1. 1.吉林大学 计算机科学与技术学院,长春 130022;
    2.长春师范大学 计算机科学与技术学院,长春 130032
  • 收稿日期:2016-01-12 出版日期:2017-03-20 发布日期:2017-03-20
  • 通讯作者: 臧雪柏(1963-),女,研究员,博士.研究方向:数据库与智能网络.E-mail:xbzang@yahoo.com.cn
  • 作者简介:赵东(1978-),男,副教授,博士研究生.研究方向:智能信息系统与嵌入式技术.E-mail:zd-hy@163.com
  • 基金资助:
    国家自然科学基金项目(61101155); 吉林省自然科学基金项目(20140101184JC); 长春市科技发展计划项目(2012091); 吉林省教育厅“十三五”科学技术研究项目(2016392).

Random forest prediction method based on optimization of fruit fly

ZHAO Dong1, 2, ZANG Xue-bai1, ZHAO Hong-wei1   

  1. 1.College of Computer Science and Technology, Jilin University,Changchun 130022,China;
    2.College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China
  • Received:2016-01-12 Online:2017-03-20 Published:2017-03-20

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摘要： 提出了一种基于果蝇算法优化的随机森林预测方法,该方法使用果蝇优化算法对随机森林的两个主要参数进行优化,构建一种优化的随机森林模型,并与现有方法进行了对比和分析。实验结果表明,本文方法不仅具有更高的识别准确度,在时间上也具有较高效率,可作为问题预测的一种有效工具。

关键词: 计算机应用, 机器学习, 果蝇优化, 随机森林

Abstract: This paper presents a random forest prediction method based on optimization of fruit fly. This method uses fruit fly optimization algorithm to optimize the two main parameters of random forest; then, constructs a random forest optimization model. The proposed method and existing methods are compared and analyzed. Experimental results show that the proposed method not only has higher recognition accuracy, but also has high efficiency in time, and can be used as an effective tool for prediction problem.

Key words: computer application, machine learning, optimization of fruit fly, random forest

中图分类号: 

  • TP393
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