DeepAI
Log In Sign Up

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

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 the memory cost, keeping the 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, 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.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/16/2019

Online Local Boosting: improving performance in online decision trees

As more data are produced each day, and faster, data stream mining is gr...
08/03/2018

Hoeffding Trees with nmin adaptation

Machine learning software accounts for a significant amount of energy co...
05/06/2022

Green Accelerated Hoeffding Tree

State-of-the-art machine learning solutions mainly focus on creating hig...
07/16/2019

FAHT: An Adaptive Fairness-aware Decision Tree Classifier

Automated data-driven decision-making systems are ubiquitous across a wi...
04/12/2016

Confidence Decision Trees via Online and Active Learning for Streaming (BIG) Data

Decision tree classifiers are a widely used tool in data stream mining. ...
02/10/2019

Hybrid Forest: A Concept Drift Aware Data Stream Mining Algorithm

Nowadays with a growing number of online controlling systems in the orga...
04/23/2015

Use of Ensembles of Fourier Spectra in Capturing Recurrent Concepts in Data Streams

In this research, we apply ensembles of Fourier encoded spectra to captu...