Improvement of Data Stream Decision Trees

The classification of data streams has become a significant and active research area. The principal characteristics of data streams are a large amount of arrival data, the high speed and rate of its arrival, and the change of their nature and distribution over time. Hoeffding Tree is a method to, in...

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Bibliographic Details
Published inInternational journal of data warehousing and mining Vol. 18; no. 1; pp. 1 - 17
Main Authors Bahloul, Sarah Nait, Abderrahim, Oussama, Amar, Aya Ichrak Benhadj, Bouhedadja, Mohammed Yacine
Format Journal Article
LanguageEnglish
Published Hershey IGI Global 01.01.2022
Subjects
Algorithms
Classification
Data transmission
Decision trees
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Summary:The classification of data streams has become a significant and active research area. The principal characteristics of data streams are a large amount of arrival data, the high speed and rate of its arrival, and the change of their nature and distribution over time. Hoeffding Tree is a method to, incrementally, build decision trees. Since its proposition in the literature, it has become one of the most popular tools of data stream classification. Several improvements have since emerged. Hoeffding Anytime Tree was recently introduced and is considered one of the most promising algorithms. It offers a higher accuracy compared to the Hoeffding Tree in most scenarios, at a small additional computational cost. In this work, the authors contribute by proposing three improvements to the Hoeffding Anytime Tree. The improvements are tested on known benchmark datasets. The experimental results show that two of the proposed variants make better usage of Hoeffding Anytime Tree’s properties. They learn faster while providing the same desired accuracy.
ISSN:1548-3924
1548-3932
DOI:10.4018/IJDWM.290889