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Dealing with memory and time constraints are current challenges when learningfrom data streams with a massive amount of data. Many algorithms have beenproposed 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 itsperformance, putting aside memory concerns by proposing memory-costlysolutions. Besides, most data stream mining solutions have been centred aroundensembles, which combine the memory costs of their weak learners, usuallyVFDTs. To reduce the memory cost, keeping the predictive performance, thisstudy proposes the Strict VFDT (SVFDT), a novel algorithm based on the VFDT.The SVFDT algorithm minimises unnecessary tree growth, substantially reducingmemory usage and keeping competitive predictive performance. Moreover, since itcreates much more shallow trees than VFDT, SVFDT can achieve a shorterprocessing time. Experiments were carried out comparing the SVFDT with the VFDTin 11 benchmark data stream datasets. This comparison assessed the trade-offbetween accuracy, memory, and processing time. Statistical analysis showed thatthe proposed algorithm obtained similar predictive performance andsignificantly reduced processing time and memory use. Thus, SVFDT is a suitableoption for data stream mining with memory and time limitations, recommended asa weak learner in ensemble-based solutions.

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