  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.
