Boosting Algorithms for Large-Scale Data and Data Batch Stream

被引：0

作者：

Yoon, Young Joo ^{[1
]}

机构：

[1] Univ Georgia, Dept Stat, 101 Cedar St, Athens, GA 30602 USA

来源：

KOREAN JOURNAL OF APPLIED STATISTICS | 2010年 / 23卷 / 01期

关键词：

AdaBoost; Arc-x4; concept drift; data stream; ensemble method; large scale data;

D O I：

暂无

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this paper, we propose boosting algorithms when data are very large or coming in batches sequentially over time. In this situation, ordinary boosting algorithm may be inappropriate because it requires the availability of all of the training set at once. To apply to large scale data or data batch stream, we modify the AdaBoost and Arc-x4. These algorithms have good results for both large scale data and data batch stream with or without concept drift on simulated data and real data sets.

引用

页码：197 / 206

页数：10

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