Strict Very Fast Decision Tree: A memory conservative algorithm for data stream mining

Victor G. Turrisi da Costa

doi:10.1016/j.patrec.2018.09.004

Strict Very Fast Decision Tree: A memory conservative algorithm for data stream mining

2018-12-01

27

OA

AI

V

Victor G. Turrisi da Costa *

A

André C. P. L. F. de Carvalho

S

Sylvio Barbon

DOI：10.1016/j.patrec.2018.09.004

原文链接

查看原文

摘要

En 中文

Dealing with memory and time constraints are current challenges when learning from data streams with a massive amount of data. Many algorithms have been proposed to handle these difficulties, among them, the Very Fast Decision Tree (VFDT) algorithm. Although the VFDT has been widely used in data stream mining, in the last years, several authors have suggested modifications to increase its performance, putting aside memory concerns by proposing memory-costly solutions. Besides, most data stream mining solutions have been centred around ensembles, which combine the memory costs of their weak learners, usually VFDTs. To reduce memory costs, keeping predictive performance, this study proposes the Strict VFDT (SVFDT), a novel algorithm based on the VFDT. The SVFDT algorithm minimises unnecessary tree growth, substantially reducing memory usage and keeping competitive predictive performance. Moreover, since it creates much more shallow trees than VFDT, the SVFDT can achieve a shorter processing time. Experiments were carried out comparing the SVFDT with the VFDT in 11 benchmark data stream datasets. This comparison assessed the trade-off between accuracy, memory, and processing time. Statistical analysis showed that the proposed algorithm obtained similar predictive performance and significantly reduced processing time and memory use. Thus, SVFDT is a suitable option for data stream mining with memory and time limitations, recommended as a weak learner in ensemble-based solutions. (C) 2018 Elsevier B.V. All rights reserved.

Keyword:

Data stream mining

Machine learning

Memory-friendly algorithm

Strict Very Fast Decision Tree: A memory conservative algorithm for data stream mining

摘要

AI总结

期刊

机构

引用论文