Enhancement of Very Fast Decision Tree for Data Stream Mining

Abstract

: Traditional machine learning (ML) algorithms use static datasets to model knowledge. Nowadays, there is an increasing demand for machine learning based solutions that can handle very huge amounts of data in the shape of streams that never stop. The Very Fast Decision Tree (VFDT) is one of the most widely utilized data stream mining algorithms (DSM), despite the fact that it wastes a huge amount of energy on trivial calculations. The machine learning community has come first in terms of accuracy and execution time while designing algorithms like this. When assessing data mining algorithms, numerous types of studies include energy usage as a crucial factor. The purpose of this research is to create a hyper model to optimize the VFDT algorithm, which reduces the waste of energy while maintaining accuracy. In the proposed method, some fixed algorithm parameters were changed to dynamic parameters after analyzing each of them separately and knowing the extent of their positive impact on reducing energy consumption in several cases in algorithm. The practical experiment was conducted on both the algorithm in its basic form and the algorithm in the proposed form on several different types of datasets in the same application environment The main advantage of the results of the proposed method compared to the results of the basic algorithm is that there was a noticeable development in the performance of the algorithm in terms of reducing its energy consumption and maintaining its accuracy levels.