Improvement of Data Stream Decision Trees

Sarah Nait Bahloul, Oussama Abderrahim, Aya Ichrak Benhadj Amar and Mohammed Yacine Bouhedadja
Additional contact information
Sarah Nait Bahloul: LSSD Laboratory, University of Science and Technology Mohamed Boudiaf, Oran, Algeria
Oussama Abderrahim: University of Science and Technology Mohamed Boudiaf, Oran, Algeria
Aya Ichrak Benhadj Amar: University of Science and Technology Mohamed Boudiaf, Oran, Algeria
Mohammed Yacine Bouhedadja: Univesrity Abou Bakr Belkaid, Tlemcen, Algeria

International Journal of Data Warehousing and Mining (IJDWM), 2022, vol. 18, issue 1, 1-17

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.

Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDWM.290889 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jdwm00:v:18:y:2022:i:1:p:1-17

Access Statistics for this article

International Journal of Data Warehousing and Mining (IJDWM) is currently edited by Eric Pardede

More articles in International Journal of Data Warehousing and Mining (IJDWM) from IGI Global
Bibliographic data for series maintained by Journal Editor ().