An Improved LS-SVR Ensemble Learning in Internet Traffic Prediction

被引：1

作者：

Li, Kunlun ^{[1
]}

Ma, Yinghui ^{[1
]}

Tian, Yongmei ^{[1
]}

Xie, Jing ^{[1
]}

机构：

[1] Hebei Univ, Coll Elect & Elect Engn, Baoding 071002, Peoples R China

来源：

FRONTIERS OF MANUFACTURING AND DESIGN SCIENCE II, PTS 1-6 | 2012年 / 121-126卷

关键词：

Ensemble learning; AdaBoost; LS-SVR; Support Vector Machines;

D O I：

10.4028/www.scientific.net/AMM.121-126.3794

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this paper, we present a new method for internet traffic forecasting based on a boosting LS-SVR algorithm. AdaBoost has been proved to be an effective method for improving the performance of weak learning algorithms and widely applied to classification problems. Inspired by it, we use LS-SVR to complete the initial training; and pay more attention on the "high error areas" in the time series; then, we use an ensemble learning algorithm to learn these areas.

引用

页码：3794 / 3798

页数：5

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