用于航班延误预测的集成式增量学习算法

U461%TP308; 为持续高效地学习不断产生的航班运行信息,提高航班延误预测模型学习新到达数据的效率,采用集成学习思想,提出了一种基于分类与回归树(classification and regression tree,CART)的增量学习算法.首先,将CART算法与Learn++算法结合实现了增量分类与回归树(incremental classification and regression tree,I-CART)算法;然后,进一步分析了基分类器间的区别和与精确度的关系,使用选择性集成算法来提高I-CART算法预测速率;最后,将该算法应用到航班延误预测中,增量地学习航班动态运行信息.实验...

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Published in北京工业大学学报 Vol. 46; no. 11; pp. 1239 - 1245
Main Authors 王丹, 王萌, 王晓曦, 杨萍
Format Journal Article
LanguageChinese
Published 北京工业大学信息学部,北京 100124%国家电网管理学院,北京 102200 01.11.2020
Subjects
选择性集成
集成学习
机器学习
增量学习
航班延误
分类与回归树(CART)算法
Online AccessGet full text
ISSN0254-0037
DOI10.11936/bjutxb2019030009

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Abstract U461%TP308; 为持续高效地学习不断产生的航班运行信息,提高航班延误预测模型学习新到达数据的效率,采用集成学习思想,提出了一种基于分类与回归树(classification and regression tree,CART)的增量学习算法.首先,将CART算法与Learn++算法结合实现了增量分类与回归树(incremental classification and regression tree,I-CART)算法;然后,进一步分析了基分类器间的区别和与精确度的关系,使用选择性集成算法来提高I-CART算法预测速率;最后,将该算法应用到航班延误预测中,增量地学习航班动态运行信息.实验结果表明,该算法有效地提高了模型预测效果.
AbstractList U461%TP308; 为持续高效地学习不断产生的航班运行信息,提高航班延误预测模型学习新到达数据的效率,采用集成学习思想,提出了一种基于分类与回归树(classification and regression tree,CART)的增量学习算法.首先,将CART算法与Learn++算法结合实现了增量分类与回归树(incremental classification and regression tree,I-CART)算法;然后,进一步分析了基分类器间的区别和与精确度的关系,使用选择性集成算法来提高I-CART算法预测速率;最后,将该算法应用到航班延误预测中,增量地学习航班动态运行信息.实验结果表明,该算法有效地提高了模型预测效果.
Author 王丹
王晓曦
杨萍
王萌
AuthorAffiliation 北京工业大学信息学部,北京 100124%国家电网管理学院,北京 102200
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WANG Meng
WANG Xiaoxi
YANG Ping
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Issue 11
Keywords 选择性集成
集成学习
机器学习
增量学习
航班延误
分类与回归树(CART)算法
Language Chinese
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