Restructuring of Hoeffding Trees for Trapezoidal Data Streams

被引：5

作者：

Schreckenberger, Christian ^{[1
]}

Glockner, Tim ^{[1
]}

Stuckenschmidt, Heiner ^{[1
]}

Bartelt, Christian ^{[2
]}

机构：

[1] Univ Mannheim, Inst Enterprise Syst, Mannheim, Germany

[2] Tech Univ Clausthal, Inst Software & Syst Engn, Clausthal Zellerfeld, Germany

来源：

20TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2020) | 2020年

关键词：

BIG DATA;

D O I：

10.1109/ICDMW51313.2020.00064

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Trapezoidal Data Streams are an emerging topic, where not only the data volume increases, but also the data dimension, i.e. new features emerge. In this paper, we address the challenges that arise from this problem by providing a novel approach to restructure and prune Hoeffding trees. We evaluate our approach on synthetic datasets, where we can show that the approach significantly improves the performance compared to the baseline of an adjusted Hoeffding tree algorithm without restructuring and pruning.

引用

页码：416 / 423

页数：8

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