改进的基于遗传算法的XGBoost模型

IF 1.2 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY Pub Date : 2020-01-01 DOI:10.1504/ijcat.2020.106571

ChenJinxiang, Zhaofeng, SunYanguang, YinYilan

引用次数: 12

摘要

提出了一种基于遗传算法的优化XGBoost模型，用于搜索最优参数组合。实验证明，改进后的算法具有较好的分类效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Improved XGBoost model based on genetic algorithm

An optimised XGBoost model based on genetic algorithm to search for optimal parameter combinations is proposed in this paper. It was proved that the improved algorithm has better classification eff...

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来源期刊

INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-

CiteScore

2.80

自引率

45.50%

发文量

期刊介绍： IJCAT addresses issues of computer applications, information and communication systems, software engineering and management, CAD/CAM/CAE, numerical analysis and simulations, finite element methods and analyses, robotics, computer applications in multimedia and new technologies, computer aided learning and training. Topics covered include: -Computer applications in engineering and technology- Computer control system design- CAD/CAM, CAE, CIM and robotics- Computer applications in knowledge-based and expert systems- Computer applications in information technology and communication- Computer-integrated material processing (CIMP)- Computer-aided learning (CAL)- Computer modelling and simulation- Synthetic approach for engineering- Man-machine interface- Software engineering and management- Management techniques and methods- Human computer interaction- Real-time systems