Improvement of Data Stream Decision Trees

Sarah Nait Bahloul, Oussama Abderrahim, Aya Ichrak Benhadj Amar, Mohammed Yacine Bouhedadja

Source Title: International Journal of Data Warehousing and Mining (IJDWM)18(1)

ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781799893684|DOI: 10.4018/IJDWM.290889

MLA

Bahloul, Sarah Nait, et al. "Improvement of Data Stream Decision Trees." IJDWM vol.18, no.1 2022: pp.1-17. http://doi.org/10.4018/IJDWM.290889

APA

Bahloul, S. N., Abderrahim, O., Amar, A. I., & Bouhedadja, M. Y. (2022). Improvement of Data Stream Decision Trees. International Journal of Data Warehousing and Mining (IJDWM), 18(1), 1-17. http://doi.org/10.4018/IJDWM.290889

Chicago

Bahloul, Sarah Nait, et al. "Improvement of Data Stream Decision Trees," International Journal of Data Warehousing and Mining (IJDWM) 18, no.1: 1-17. http://doi.org/10.4018/IJDWM.290889

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Abstract

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.